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[1]
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Fetal state health monitoring using novel Enhanced Binary Bat Algorithm
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Computers and Electrical Engineering,
2022 |
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[2]
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Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification
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Applied Bionics and …,
2022 |
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[3]
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Investigating association relationship between fetal heart rate parameters from cardiotocography employing multi-objective evolutionary algorithms
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International Journal of Information …,
2022 |
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[4]
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Comparative Analysis of Machine Learning Models For Early Detection of Fetal Disease using Feature Extraction
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2022 IEEE 11th …,
2022 |
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[5]
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Evaluation of Diagnostic Performance of Machine Learning Algorithms to Classify the Fetal Heart Rate Baseline From Cardiotocograph
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International Journal of …,
2022 |
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[6]
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Optimized Classification of fetal state health using GWO and WOA
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2022 IEEE 9th International Conference …,
2022 |
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A Comparative Analysis of Neural Network and Decision Tree Model for Detecting Result Anomalies
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Open Access Library Journal,
2022 |
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[8]
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Research Article Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification
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2022 |
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Deep Learning Classification of Fetal Cardiotocography Data with Differential Privacy
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2022 International Conference on …,
2022 |
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[10]
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Automatic detection of fetal health status from cardiotocography data using machine learning algorithms
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Journal of Bangladesh …,
2021 |
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[11]
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A comprehensive review of techniques for processing and analyzing fetal heart rate signals
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Sensors,
2021 |
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An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification
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Expert Systems with Applications,
2021 |
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Multiparametric investigation of dynamics in fetal heart rate signals
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Bioengineering,
2021 |
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A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning
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… Conference on Data Mining and Big …,
2021 |
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Effective techniques for intelligent cardiotocography interpretation using XGB-RF feature selection and stacking fusion
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2021 IEEE …,
2021 |
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Towards Making More Reliable Cardiotocogram Data Prediction with Limited Expert Knowledge: Exploiting Unlabeled Data with Semi-supervised Boosting Method
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… Conference on Data Mining and Big …,
2021 |
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Extreme Learning Machines with feature selection using GA for effective prediction of fetal heart disease: A Novel Approach
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Informatica,
2021 |
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[18]
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Classification of Imbalanced Fetal Health Data by PSO Based Ensemble Recursive Feature Elimination ANN
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… Conference on Swarm …,
2021 |
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[19]
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Intelligent classification of antepartum cardiotocography model based on deep forest
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2021 |
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[20]
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Analysis and Interpretation of Uterine Contraction Signals Using Artificial Intelligence
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2021 |
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[21]
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An intelligent prenatal screening system for the prediction of Trisomy-21
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2021 |
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[22]
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Machine Learning based Emotion classification in the COVID-19 Real World Worry Dataset
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Computer Science,
2021 |
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[23]
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Data Mining for the Prediction of Fetal Malformation Through Cardiotocography Data
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2021 |
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[24]
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An Improved Decision Tree Classification Approach for Expectation of Cardiotocogram
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2021 |
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[25]
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Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques
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2021 |
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[26]
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Automatic fetal distress assessment during labor based on modal and parametrical analysis of the cardiotocographic recording
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2020 |
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[27]
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Fusing fine-tuned deep features for recognizing different tympanic membranes
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2020 |
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[28]
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Classifying the type of delivery from cardiotocographic signals: a machine learning approach
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2020 |
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[29]
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Intelligent Antenatal Fetal Monitoring Model Based on Adaptive Neuro-Fuzzy Inference System Through
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2020 |
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[30]
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Ensemble based technique for the assessment of fetal health using cardiotocograph–a case study with standard feature reduction techniques
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2020 |
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[31]
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Classification of the Cardiotocogram Data for Anticipation of Fetal Risks using Bagging Ensemble Classifier
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2020 |
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[32]
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Classification and Feature Selection Approaches for Cardiotocography by Machine Learning Techniques
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2020 |
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[33]
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Fetal Health Status Classification Using MOGA-CD Based Feature Selection Approach
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2020 |
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[34]
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Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals
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2020 |
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[35]
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Intelligent Antenatal Fetal Monitoring Model Based on Adaptive Neuro-Fuzzy Inference System Through Cardiotocography
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2020 |
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[36]
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Deteksi Dini Penyakit pada Daun Stroberi Berbasis Pengolahan Citra
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2020 |
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[37]
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Abnormal Foetuses Classification Based on Cardiotocography Recordings Using Machine Learning and Deep Learning Algorithms
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2020 |
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Machine Learning Analysis for Remote Prenatal Care
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2020 |
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[39]
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Automatic Classification of Antepartum Cardiotocography Using Fuzzy Clustering and Adaptive Neuro-Fuzzy Inference System
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2020 |
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Cardiotocography Class Status Prediction Using Machine Learning Techniques.
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2019 |
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Analysis Of Cardiotocogram Data For Fetal Distress Determination
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Think India Journal,
2019 |
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Classification of Elderly Group with Hypertension for Preventing Cardiovascular Disease Complication
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2019 |
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Exploring Fetal Health Status Using an Association Based Classification Approach
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2019 |
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О «Тектонических сдвигах»(или их отсутствии?) драйверов международной мощи и влияния: опыт дискриминантного анализа
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2019 |
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Fuzzy classifier based on clustering with pairs of ε-hyperballs and its application to support fetal state assessment
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Expert Systems with Applications,
2019 |
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[46]
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Cardiotocography Analysis for Fetal State Classification Using Machine Learning Algorithms
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2019 |
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[47]
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Cardiotocography Class Status Prediction Using Machine Learning Techniques
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2019 |
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[48]
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Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
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2019 |
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KLASIFIKASI DIAGNOSIS MELAHIRKAN DENGAN METODE SESAR MENGGUNAKAN NEURAL NETWORK
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2019 |
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[50]
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Профілактика дистресу плода у вагітних з гіпертензивними розладами
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2019 |
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[51]
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Efficacy of Machine Learning in Predicting the Kind of Delivery by Cardiotocography
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2019 |
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[52]
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Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest
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2019 |
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[53]
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Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment
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2019 |
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[54]
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SEM-based study for interpretability of intelligent prenatal fetal monitoring models
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2019 |
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[55]
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Knowledge Management System for Fetal Movement during Pregnancy
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2018 |
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[56]
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Analysis of Effects of Most Influential Risk Factors on Gestational Diabetes Mellitus
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2018 |
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[57]
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Correlating cardiotocography, apgar score and cord blood ph after caesarean section for fetal distress at Rahima Moosa mother and child hospital
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Thesis,
2018 |
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[58]
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Cardiotocography Data Set Classification with Extreme Learning Machine
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International Conference on Advanced Technologies, Computer Engineering and Science,
2018 |
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[59]
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Automatic classification of fetal heart rate based on convolutional neural network
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2018 |
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[60]
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Detección difusa del estado de bienestar fetal anteparto en embarazos de alto riesgo
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2018 |
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[61]
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Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment
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Computers in Biology and Medicine,
2018 |
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[62]
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Prediction of Fetal Health State during Pregnancy: A Survey
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2018 |
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[63]
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Divide and conquer! Data-mining tools and sequential multivariate analysis to search for diagnostic morphological characters within a plant polyploid complex …
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PLOS ONE,
2018 |
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[64]
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Fetal health status prediction based on maternal clinical history using machine learning techniques
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Computer Methods and Programs in Biomedicine,
2018 |
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2018 |
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[66]
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Estimation of fetal weight by fundal height measurement using regression and decision tree models in Ahwaz Imam Khomeini Hospital in winter 2014
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2018 |
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[67]
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5. Analysis of Effects of Most Influential Risk Factors on Gestational Diabetes Mellitus
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2018 |
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[68]
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Applying deep learning for adverse pregnancy outcome detection with pre-pregnancy health data
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2018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018),
2018 |
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[69]
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A study of artificial neural network training algorithms for classification of cardiotocography signals
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Bitlis Eren University Journal of Science and Technology,
2017 |
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[70]
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Modelling Characteristics of Eye Movement Analysis for stress detection–Performance Analysis using Decision tree approach
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International Journal of Engineering and Innovative Technology (IJEIT),
2017 |
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[71]
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Asian Research Consortium
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2017 |
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[72]
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Multi-label classification methods for improving comorbidities identification
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Computers in Biology and Medicine,
2017 |
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Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
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2017 |
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Fetal State Assessment Based on Cardiotocography Parameters Using PCA and AdaBoost
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2017 |
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A Hybrid Filter-Wrapper Attribute Reduction Approach For Fetal Risk Anticipation
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Asian Journal of Research in Social Sciences and Humanities,
2017 |
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Cardiotocography-A Comparative Study between Support Vector Machine and Decision Tree Algorithms
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International Journal of Trend in Research and Development,
2017 |
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Decision tree to analyze the cardiotocogram data for fetal distress determination
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2017 |
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Kardiyotokografi işaretlerinin analizi ve makine öğrenmesi teknikleri ile sınıflandırılması
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2017 |
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[79]
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Opposition based firefly algorithm optimized feature subset selection approach for fetal risk anticipation
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2016 |
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A Decision Support System for Determination of Fetal Well-Being from Cardiotocogram Data
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2016 |
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KARDİOTOKOGRAM VERİSİNDEN FETAL İYİLİK HALİNİN BELİRLENMESİ İÇİN BİR KARAR DESTEK SİSTEMİ
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Uluda? University Journal of The Faculty of Engineering,
2016 |
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ANALISA NILAI UJIAN MASUK STT WASTUKANCANA BERDASARKAN NILAI UJIAN NASIONAL MENGGUNAKAN METODE BACKPROPAGATION
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2016 |
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Analytical Study of Selected Classification Algorithms for Clinical Dataset
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Indian Journal of Science and Technology,
2016 |
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Improving Children Diagnostics by Efficient Multi-label Classification Method
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Information Technologies in Medicine,
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Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
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Journal of Computer and Communications,
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Classification of Cardiotocography Data with WEKA
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2016 |
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Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis
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Journal of Biomedical Informatics,
2016 |
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OPPOSITION-BASED FIREFLY ALGORITHM OPTIMIZED FEATURE SUBSET SELECTION APPROACH FOR FETAL RISK ANTICIPATION
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An International Journal (MLAIJ) ,
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Classification model for cardiotocographies
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Application of Machine Learning Techniques to classify Fetal Hypoxia
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2016 |
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Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks
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Journal of Medical and Biological Engineering,
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Machine learning algorithms for the creation of clinical healthcare enterprise systems
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Enterprise Information Systems,
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Perception of Bankers on Factors Causing Non Performing Assets in the Indian Banking Sector
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Asian Journal of Research in Social Sciences and Humanities,
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Methods of classification of the cardiotocogram
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Applied Soft Computing,
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Distance Measures in Bioinformatics
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Genetic Algorithm based feature subset selection for fetal state classification
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Journal of Communications Technology, Electronics and Computer Science,
2015 |
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Decision Trees Based Classification of Cardiotocograms Using Bagging Approach
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2014 |
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Research journal of Pharmaceutical, Biological and Chemical Sciences,
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Human Fetus Health Classification on Cardiotocographic Data Using Random Forests
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Intelligent Data analysis and its Applications, Volume II. Springer International Publishing,
2014 |
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Classification of cardiotocography records by random forest
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Telecommunications and Signal Processing (TSP), 2013 36th International Conference on. IEEE,
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