scholarly journals Maternal Parenting Stress in the Face of Early Regulatory Disorders in Infancy: A Machine Learning Approach to Identify What Matters Most

2021 ◽  
Vol 12 ◽  
Author(s):  
Anna K. Georg ◽  
Paul Schröder-Pfeifer ◽  
Manfred Cierpka ◽  
Svenja Taubner
Keyword(s):  
Machine Learning ◽  
Psychological Distress ◽  
Parenting Stress ◽  
Self Efficacy ◽  
Physical Development ◽  
Stress Index ◽  
Empirical Literature ◽  
Data Set ◽  
Wide Range ◽  
Regulatory Disorders

Objective: Early regulatory disorders (ERD) in infancy are typically associated with high parenting stress (PS). Theoretical and empirical literature suggests a wide range of factors that may contribute to PS related to ERD. The aim of this study was to identify key predictors of maternal PS within a large predictor data set in a sample of N = 135 mothers of infants diagnosed with ERD.Methods: We used machine learning to identify relevant predictors. Maternal PS was assessed with the Parenting Stress Index. The multivariate dataset assessed cross-sectionally consisted of 464 self-reported and clinically rated variables covering mother-reported psychological distress, maternal self-efficacy, parental reflective functioning, socio-demographics, each parent's history of illness, recent significant life events, former miscarriage/abortion, pregnancy, obstetric history, infants' medical history, development, and social environment. Variables were drawn from behavioral diaries on regulatory symptoms and parental co-regulative behavior as well as a clinical interview which was utilized to diagnose ERD and to assess clinically rated regulatory symptoms, quality of parent–infant relationship, organic/biological and psychosocial risks, and social–emotional functioning.Results: The final prediction model identified 11 important variables summing up to the areas maternal self-efficacy, psychological distress (particularly depression and anger-hostility), infant regulatory symptoms (particularly duration of fussing/crying), and age-appropriate physical development. The RMSE (i.e., prediction accuracy) of the final model applied to the test set was 21.72 (R2 = 0.58).Conclusions: This study suggests that among behavioral, environmental, developmental, parent–infant relationship, and mental health variables, a mother's higher self-efficacy, psychological distress symptoms particularly depression and anger symptoms, symptoms in the child particularly fussing/crying symptoms, and age-inappropriate physical development are associated with higher maternal PS. With these factors identified, clinicians may more efficiently assess a mother's PS related to ERD in a low-risk help-seeking sample.

scholarly journals Tree‐Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level

2020 ◽  
Vol 9 (22) ◽  
Author(s):  
Liangyuan Hu ◽  
Bian Liu ◽  
Jiayi Ji ◽  
Yan Li
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Health ◽  
Black People ◽  
The United States ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Wide Range ◽  
Neighborhood Level

Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community‐based interventions have been shown to be both effective and cost‐effective in preventing cardiovascular disease. There is a dearth of robust studies identifying the key determinants of cardiovascular disease and the underlying effect mechanisms at the neighborhood level. We aim to contribute to the evidence base for neighborhood cardiovascular health research. Methods and Results We created a new neighborhood health data set at the census tract level by integrating 4 types of potential predictors, including unhealthy behaviors, prevention measures, sociodemographic factors, and environmental measures from multiple data sources. We used 4 tree‐based machine learning techniques to identify the most critical neighborhood‐level factors in predicting the neighborhood‐level prevalence of stroke, and compared their predictive performance for variable selection. We further quantified the effects of the identified determinants on stroke prevalence using a Bayesian linear regression model. Of the 5 most important predictors identified by our method, higher prevalence of low physical activity, larger share of older adults, higher percentage of non‐Hispanic Black people, and higher ozone levels were associated with higher prevalence of stroke at the neighborhood level. Higher median household income was linked to lower prevalence. The most important interaction term showed an exacerbated adverse effect of aging and low physical activity on the neighborhood‐level prevalence of stroke. Conclusions Tree‐based machine learning provides insights into underlying drivers of neighborhood cardiovascular health by discovering the most important determinants from a wide range of factors in an agnostic, data‐driven, and reproducible way. The identified major determinants and the interactive mechanism can be used to prioritize and allocate resources to optimize community‐level interventions for stroke prevention.

scholarly journals Prediction of Lung Cancer Risk using Random Forest Algorithm Based on Kaggle Data Set

2020 ◽  
Vol 8 (6) ◽  
pp. 1623-1630
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Naive Bayes ◽  
Early Stage ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Wide Range

As huge amount of data accumulating currently, Challenges to draw out the required amount of data from available information is needed. Machine learning contributes to various fields. The fast-growing population caused the evolution of a wide range of diseases. This intern resulted in the need for the machine learning model that uses the patient's datasets. From different sources of datasets analysis, cancer is the most hazardous disease, it may cause the death of the forbearer. The outcome of the conducted surveys states cancer can be nearly cured in the initial stages and it may also cause the death of an affected person in later stages. One of the major types of cancer is lung cancer. It highly depends on the past data which requires detection in early stages. The recommended work is based on the machine learning algorithm for grouping the individual details into categories to predict whether they are going to expose to cancer in the early stage itself. Random forest algorithm is implemented, it results in more efficiency of 97% compare to KNN and Naive Bayes. Further, the KNN algorithm doesn't learn anything from training data but uses it for classification. Naive Bayes results in the inaccuracy of prediction. The proposed system is for predicting the chances of lung cancer by displaying three levels namely low, medium, and high. Thus, mortality rates can be reduced significantly.

Evaluation of Machine Learning Approaches for Automated Diagnosis of COVID-19 using X-Ray images (Preprint)

2020 ◽  
Author(s):  
Mazin Mohammed ◽  
Karrar Hameed Abdulkareem ◽  
Mashael S. Maashi ◽  
Salama A. Mostafa A. Mostafa ◽  
Abdullah Baz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Method ◽  
Learning Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
X Ray ◽  
Wide Range ◽  
Artificial Neural Network Ann

BACKGROUND In most recent times, global concern has been caused by a coronavirus (COVID19), which is considered a global health threat due to its rapid spread across the globe. Machine learning (ML) is a computational method that can be used to automatically learn from experience and improve the accuracy of predictions. OBJECTIVE In this study, the use of machine learning has been applied to Coronavirus dataset of 50 X-ray images to enable the development of directions and detection modalities with risk causes.The dataset contains a wide range of samples of COVID-19 cases alongside SARS, MERS, and ARDS. The experiment was carried out using a total of 50 X-ray images, out of which 25 images were that of positive COVIDE-19 cases, while the other 25 were normal cases. METHODS An orange tool has been used for data manipulation. To be able to classify patients as carriers of Coronavirus and non-Coronavirus carriers, this tool has been employed in developing and analysing seven types of predictive models. Models such as , artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbour (k-NN), Decision Tree (DT), and CN2 rule inducer were used in this study.Furthermore, the standard InceptionV3 model has been used for feature extraction target. RESULTS The various machine learning techniques that have been trained on coronavirus disease 2019 (COVID-19) dataset with improved ML techniques parameters. The data set was divided into two parts, which are training and testing. The model was trained using 70% of the dataset, while the remaining 30% was used to test the model. The results show that the improved SVM achieved a F1 of 97% and an accuracy of 98%. CONCLUSIONS :. In this study, seven models have been developed to aid the detection of coronavirus. In such cases, the learning performance can be improved through knowledge transfer, whereby time-consuming data labelling efforts are not required.the evaluations of all the models are done in terms of different parameters. it can be concluded that all the models performed well, but the SVM demonstrated the best result for accuracy metric. Future work will compare classical approaches with deep learning ones and try to obtain better results. CLINICALTRIAL None

scholarly journals Predictive and generative machine learning models for photonic crystals

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4183-4192 ◽  
Author(s):  
Thomas Christensen ◽  
Charlotte Loh ◽  
Stjepan Picek ◽  
Domagoj Jakobović ◽  
Li Jing ◽  
...  
Keyword(s):  
Machine Learning ◽  
Photonic Crystals ◽  
Optimization Techniques ◽  
Inverse Design ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Test Bed ◽  
Data Set ◽  
Wide Range

AbstractThe prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences.

scholarly journals Comparison of approaches for intrusion detection in substations using the IEC 60870-5-104 protocol

2020 ◽  
Vol 3 (S1) ◽  
Author(s):  
Michael Egger ◽  
Günther Eibl ◽  
Dominik Engel
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Smart Grids ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Normal Operation ◽  
International Electrotechnical Commission ◽  
Learning Approaches ◽  
Data Set ◽  
Wide Range

Abstract Electrical networks of transmission system operators are mostly built up as isolated networks without access to the Internet. With the increasing popularity of smart grids, securing the communication network has become more important to avoid cyber-attacks that could result in possible power outages. For misuse detection, signature-based approaches are already in use and special rules for a wide range of protocols have been developed. However, one big disadvantage of signature-based intrusion detection is that zero-day exploits cannot be detected. Machine-learning-based anomaly detection methods have the potential to achieve that. In this paper, various such methods for intrusion detection in substations, which use the asynchronous communication protocol International Electrotechnical Commission (IEC) 60870-5-104, are tested and compared. The evaluation of the proposed methods is performed by applying them to a data set which includes normal operation traffic and four different attacks. While the results of supervised and semi-supervised machine learning approaches are rather encouraging, the unsupervised and signature-based methods suffer from general bad performance and had difficulties to detect some attacks.

scholarly journals Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas Martynec ◽  
Christos Karapanagiotis ◽  
Sabine H. L. Klapp ◽  
Stefan Kowarik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
Diffusion Barriers ◽  
Binding Energies ◽  
Atomic Scale ◽  
Training Data ◽  
Data Set ◽  
Wide Range

AbstractMachine learning is playing an increasing role in the discovery of new materials and may also facilitate the search for optimum growth conditions for crystals and thin films. Here, we perform kinetic Monte-Carlo simulations of sub-monolayer growth. We consider a generic homoepitaxial growth scenario that covers a wide range of conditions with different diffusion barriers (0.4–0.55 eV) and lateral binding energies (0.1–0.4 eV). These simulations are used as a training data set for a convolutional neural network that can predict diffusion barriers and binding energies. Specifically, a single Monte-Carlo image of the morphology is sufficient to determine the energy barriers with an accuracy of approximately 10 meV and the neural network is tolerant to images with noise and lower than atomic-scale resolution. We believe this new machine learning method will be useful for fundamental studies of growth kinetics and growth optimization through better knowledge of microscopic parameters.

scholarly journals Threshold Voltage for Digital Residual Current Circuit Breaker Based on Tensor Flow

2021 ◽  
Vol 56 (1) ◽  
Author(s):  
Erwin Sutanto ◽  
Hammam Abror Ali ◽  
Yhosep Gita Yhun Yhuwana ◽  
Muhammad Aziz
Keyword(s):  
Machine Learning ◽  
Leakage Current ◽  
Threshold Voltage ◽  
Circuit Breaker ◽  
Current Data ◽  
Residual Current ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Wide Range ◽  
The Right

The article describes a new way to define the threshold voltage for Machine Learning-based Digital Residual Current Circuit Breaker (RCCB), enabling the right cut-off point. Using the described methods, the authors obtained a gap to common mid voltage being around 0.5 V. The proposed technique is illustrated with three different loads of 3W, 5W, and 9W as the scope of this work. The authors try to apply it in Residual Current Circuit Breaker (RCCB). It could be useful in a hospital with a limited number of technicians to maintain various machines quickly. This work tries to realize a machine that could find out the best condition to cut off the electricity when there is any leakage current but keep the supply if it is still under tolerance. This allows improving the mistake of the midpoint about 16.97% over its wide range. The effectiveness of Python libraries usage realized the Artificial Neural Network (ANN) implementation as one of machine-learning algorithms. The learning process is applied to the measured leakage current data set. It goes with input preprocessing, training, testing, and data analysis. From all of those steps, it is possible to determine the induction voltage threshold at 1.080 from 3.3V as its maximum value with a negligible loss value of 0.0006. By comparing the value with a reference, it can be concluded that this method could be used in a real situation.

scholarly journals A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

2021 ◽  
pp. 45-59
Author(s):  
Shler Farhad Khorshid ◽  
Adnan Mohsin Abdulazeez ◽  
Amira Bibo Sallow
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Wide Range

Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

scholarly journals A comprehensive study on battery electric modeling approaches based on machine learning

2021 ◽  
Vol 4 (S3) ◽  
Author(s):  
Felix Heinrich ◽  
Patrick Klapper ◽  
Marco Pruckner
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Model Complexity ◽  
Series Data ◽  
Data Set ◽  
Regression Methods ◽  
Wide Range ◽  
Electric Modeling ◽  
Conventional Regression ◽  
Comprehensive Study

AbstractBattery electric modeling is a central aspect to improve the battery development process as well as to monitor battery system behavior. Besides conventional physical models, machine learning methods show great potential to learn this task using in-vehicle data. However, the performance of data-driven approaches differs significantly depending on their application and utilized data set. Hence, a comparison among these methods is required beforehand to select the optimal candidate for a given task.In this work, we address this problem and evaluate the strengths and weaknesses of a wide range of possible machine learning approaches for battery electric modeling. In a comprehensive study, various conventional regression methods and neural networks are analyzed. Each method is trained and optimized based on a large and qualitative data set of automotive driving profiles. In order to account for the influence of time-dependent battery processes, both low pass filters and sliding window approaches are investigated.As a result, neural networks are found to be superior compared to conventional regression methods in terms of accuracy and model complexity. In particular, Feedforward and Convolutional Neural Networks provide the smallest average error deviations of around 0.16%, which corresponds to an RMSE of 5.57mV on battery cell level. With automotive time series data as focus, neural networks additionally benefit from their ability to learn continuously. This key capability keeps the battery models updated at low computational costs and accounts for changing electrical behavior as the battery ages during operation.

Machine-learning algorithm for estimating oil-recovery factor using a combination of engineering and stratigraphic dependent parameters

2019 ◽  
Vol 7 (3) ◽  
pp. SE151-SE159 ◽  
Author(s):  
Kachalla Aliyuda ◽  
John Howell
Keyword(s):  
Machine Learning ◽  
Mean Square Error ◽  
North Sea ◽  
Recovery Factor ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Mean Square ◽  
Data Set ◽  
Wide Range ◽  
Testing Set

The methods used to estimate recovery factor change through the life cycle of a field. During appraisal, prior to development when there are no production data, we typically rely on analog fields and empirical methods. Given the absence of a perfect analog, these methods are typically associated with a wide range of uncertainty. During plateau, recovery factors are typically associated with simulation and dynamic modeling, whereas in later field life, once the field drops off the plateau, a decline curve analysis is also used. The use of different methods during different stages of the field life leads to uncertainty and potential inconsistencies in recovery estimates. A wide range of interacting, partially related, reservoir and production variables controls the production and recovery factor. Machine learning allows more complex multivariate analysis that can be used to investigate the roles of these variables using a training data set and then to ultimately predict future performance in fields. To investigate this approach, we used a data set consisting of producing reservoirs all of which are at plateau or in decline to train a series of machine-learning algorithms that can potentially predict the recovery factor with minimal percentage error. The database for this study consists of categorical and numerical properties for 93 reservoirs from the Norwegian Continental Shelf. Of these, 75 are from the Norwegian Sea, the Norwegian North Sea, and the Barents Sea, whereas the remaining 18 reservoirs are from the Viking Graben in the UK sector of the North Sea. The data set was divided into training and testing sets: The training set comprised approximately 80% of the total data, and the remaining 20% was the testing set. Linear regression models and a support vector machine (SVM) models were trained with all parameters in the data set (30 parameters); then with the 16 most influential parameters in the data set, the performance of these models was compared from results of fivefold crossvalidation. SVM training using a combination of 16 geologic/engineering parameters models with Gaussian kernel function has a root-mean-square error of 0.12, mean square error of 0.01, and [Formula: see text]-squared of 0.76. This model was tested on 18 reservoirs from the testing set; the test results are very similar to crossvalidation results during models training phase, suggesting that this method can potentially be used to predict the future recovery factor.

Sign in / Sign up

Export Citation Format

Share Document