scholarly journals Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 422 ◽  
Author(s):  
Xosé Fernández-Fuentes ◽  
David Mera ◽  
Andrés Gómez ◽  
Ignacio Vidal-Franco
Keyword(s):  
Machine Learning ◽  
Inverse Problem ◽  
Computation Time ◽  
Percentage Error ◽  
Problem Solver ◽  
Impedance Tomography ◽  
Quantitative Metrics ◽  
Machine Learning Approach ◽  
Primal Dual ◽  
Internal Body

Different industrial and medical situations require the non-invasive extraction of information from the inside of bodies. This is usually done through tomographic methods that generate images based on internal body properties. However, the image reconstruction involves a mathematical inverse problem, for which accurate resolution demands large computation time and capacity. In this paper we explore the use of Machine Learning to develop an accurate solver for reconstructing Electrical Impedance Tomography images in real-time. We compare the results with the Iterative Gauss-Newton and the Primal Dual Interior Point Method, which are both largely used and well-validated solvers. The approaches were compared from the qualitative as well as the quantitative viewpoints. The former was focused on correctly detecting the internal body features. The latter was based on accurately predicting internal property distributions. Experiments revealed that our approach achieved better accuracy and Cohen’s kappa coefficient (97.57% and 94.60% respectively) from the qualitative viewpoint. Moreover, it also obtained better quantitative metrics with a Mean Absolute Percentage Error of 18.28%. Experiments confirmed that Neural Networks algorithms can reconstruct internal body properties with high accuracy, so they would be able to replace more complex and slower alternatives.

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

scholarly journals Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2328 ◽  
Author(s):  
Md Shafiullah ◽  
M. Abido ◽  
Taher Abdel-Fattah
Keyword(s):  
Machine Learning ◽  
Fault Location ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Approach ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Performance Indices ◽  
Machine Learning Approach ◽  
Distribution Grids

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

scholarly journals Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

2020 ◽  
Vol 22 (40) ◽  
pp. 22889-22899
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian R. Shelton ◽  
Bryan M. Wong
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Inverse Problem ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Cost Effective ◽  
Quantum Systems ◽  
Time Dependent ◽  
Learning Approach ◽  
Machine Learning Approach

Deep neural networks are a cost-effective machine-learning approach for solving the inverse problem of constructing electromagnetic fields that enable desired transitions in quantum systems.

New CNF Features and Formula Classification

10.29007/b8t1 ◽  
2018 ◽  
Author(s):  
Enrique Alfonso ◽  
Norbert Manthey
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
Computation Time ◽  
The Other ◽  
Learning Approach ◽  
Sat Solvers ◽  
Random Decision Forests ◽  
Machine Learning Approach ◽  
Sat Solver ◽  
Cnf Formulas

In this paper we first present three new features for classifying CNF formulas. These features are based on the structural information of the formula and consider AND-gates as well as exactly-one constraints. Next, we use these features to construct a machine learning approach to select a SAT solver configuration for CNF formulas with random decision forests. Based on this classification task we can show that our new features are useful compared to existing features. Since the computation time for these features is small, the constructed classifier improves the performance of the SAT solvers on application and hand crafted benchmarks. On the other hand, the comparison shows that the set of new features also results in a better classification.

scholarly journals Reconstruction of Tomographic Images through Machine Learning Techniques

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1172
Author(s):  
Xosé Fernández-Fuentes ◽  
David Mera ◽  
Andrés Gómez
Keyword(s):  
Machine Learning ◽  
Inverse Problem ◽  
Electrical Potential ◽  
The Body ◽  
Machine Learning Techniques ◽  
Electrical Potential Difference ◽  
Basic Operation ◽  
Impedance Tomography ◽  
Tomographic Images ◽  
Learning Techniques

Some problems in the field of health or industry require to obtain information from the inside of a body without using invasive methods. Some techniques are able to get qualitative images. However, these images are not enough to solve some problems that require an accurate knowledge. Normally, the tomography processes are used to explore inside of a body. In this particular case, we are using the method called Electrical Impedance Tomography (EIT). The basic operation of this method is as follows: (1) The electrical potential difference is measured in the electrodes placed around the body. This part is known as forward model. (2) Get information from the inside of the body using the measured voltages. This problem is known as inverse problem. There are several approximations to solve this inverse problem. However, these solutions are focused on obtaining qualitative images. In this paper, we show the main challenges of how to obtain quantitative knowledge when Machine Learning techniques are used to solve this inverse problem.

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