Multilayer Feed-forward Artificial Neural Network Research Articles

Artificial intelligence prediction of the effect of rehabilitation in whiplash associated disorder

The active cervical range of motion (aROM) is assessed by clinicians to inform their decision-making. Even with the ability of neck motion to discriminate injured from non-injured subjects, the mechanisms to explain recovery or persistence of WAD remain unclear. There are few studies of ROM examinations with precision tools using kinematics as predictive factors of recovery rate. The present paper will evaluate the performance of an artificial neural network (ANN) using kinematic variables to predict the overall change of aROM after a period of rehabilitation in WAD patients. To achieve this goal the neck kinematics of a cohort of 1082 WAD patients (55.1% females), with mean age 37.68 (SD 12.88) years old, from across Spain were used. Prediction variables were the kinematics recorded by the EBI® 5 in routine biomechanical assessments of these patients. These include normalized ROM, speed to peak and ROM coefficient of variation. The improvement of aROM was represented by the Neck Functional Holistic Analysis Score (NFHAS). A supervised multi-layer feed-forward ANN was created to predict the change in NFHAS. The selected architecture of the ANN showed a mean squared error of 308.07–272.75 confidence interval for a 95% in the Monte Carlo cross validation. The performance of the ANN was tested with a subsample of patients not used in the training. This comparison resulted in a medium correlation with R = 0.5. The trained neural network to predict the expected difference in NFHAS between baseline and follow up showed modest results. While the overall performance is moderately correlated, the error of this prediction is still too large to use the method in clinical practice. The addition of other clinically relevant factors could further improve prediction performance.

Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: effects of post-reconstruction methods in a dual-center study

ObjectivesTo evaluate the performance of radiomic features extracted from high-resolution computed tomography (HRCT) for the differentiation between cholesteatoma and middle ear inflammation (MEI), and to investigate the impact of post-reconstruction harmonization and data resampling.MethodsOne hundred patients were included in this retrospective dual-center study: 48 with histology-proven cholesteatoma (center A: 23; center B: 25) and 52 with MEI (A: 27; B: 25). Radiomic features (co-occurrence and run-length matrix, absolute gradient, autoregressive model, Haar wavelet transform) were extracted from manually defined 2D-ROIs. The ten best features for lesion differentiation were selected using probability of error and average correlation coefficients. A multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used for radiomics-based classification, with histopathology serving as the reference standard (70% of cases for training, 30% for validation). The analysis was performed five times each on (a) unmodified data and on data that were (b) resampled to the same matrix size, and (c) corrected for acquisition protocol differences using ComBat harmonization.ResultsUsing unmodified data, the MLP-ANN classification yielded an overall median area under the receiver operating characteristic curve (AUC) of 0.78 (0.72–0.84). Using original data from center A and resampled data from center B, an overall median AUC of 0.88 (0.82–0.99) was yielded, while using ComBat harmonized data, an overall median AUC of 0.89 (0.79–0.92) was revealed.ConclusionRadiomic features extracted from HRCT differentiate between cholesteatoma and MEI. When using multi-centric data obtained with differences in CT acquisition parameters, data resampling and ComBat post-reconstruction harmonization clearly improve radiomics-based lesion classification.Key Points• Unenhanced high-resolution CT coupled with radiomics analysis may be useful for the differentiation between cholesteatoma and middle ear inflammation.• Pooling of data extracted from inhomogeneous CT datasets does not appear meaningful without further post-processing.• When using multi-centric CT data obtained with differences in acquisition parameters, post-reconstruction harmonization and data resampling clearly improve radiomics-based soft-tissue differentiation.

Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

In recent years, there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields, and the demand for accurate machining of such composite materials has grown accordingly. In this paper, a feed-forward multi-layered artificial neural network (ANN) roughness prediction model, using the Levenberg-Marquardt backpropagation training algorithm, is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials. Milling experiments were conducted on a computer numerical control (CNC) milling machine with polycrystalline diamond (PCD) tools to acquire data for training the ANN roughness prediction model. Four cutting parameters were considered in these experiments: cutting speed, depth of cut, feed rate, and volume fraction of SiC. These parameters were also used as inputs for the ANN roughness prediction model. The output of the model was the average surface roughness of the machined workpiece. A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters, with a 2.08% mean relative error. Moreover, a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91% mean relative error was developed based on the ANN roughness prediction model. Finally, a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.

Experimental and ANN modeling study on microwave dried onion slices

The present work deals mainly with dehydration characteristics of onion slices. Microwave power levels of 100, 350, 550 and 750 W was practiced to dry onion slices with thicknesses of 2.5, 5, 7.5 and 10 mm. The results showed that moisture diffusivity and specific energy consumption of the process increased with both increasing microwave power and the samples thickness, and ranged from 0.82 × 10−8 to 6.13 × 10−8 m2 s−1 and from 0.82 to 5.43 MJ kg−1 water, respectively. The average activation energy varied in the range of 1.28–1.77. Furthermore, for simulation of drying process and to predict the moisture removal behavior of the samples, multi-layer feed-forward (MLF) artificial neural network (ANN) was employed. Practicing different networks and based on statistical parameters, the best topology, transfer functions and training algorithms were determined. The results revealed that, as a powerful tool, ANN modeling could be effectively used to predict drying kinetics and determine the moisture content of the samples.

Parameterized nonintrusive reduced‐order model for general unsteady flow problems using artificial neural networks

AbstractA nonintrusive reduced‐order model for nonlinear parametric flow problems is developed. It is based on extracting a reduced‐order basis from high‐order snapshots via proper orthogonal decomposition and using multilayered feedforward artificial neural networks to approximate the reduced‐order coefficients. The model is a generic and efficient approach for the reduction of time‐dependent parametric systems, including those described by partial differential equations. Since it is nonintrusive, it is independent of the high‐order computational method and can be used together with black‐box solvers. Numerical studies are presented for steady‐state isentropic nozzle flow with geometric parameterization and unsteady parameterized viscous Burgers equation. An adaptive sampling strategy is proposed to increase the quality of the neural network approximation while minimizing the required number of parameter samples and, as a direct consequence, the number of high‐order snapshots and the size of the network training set. Results confirm the accuracy of the nonintrusive approach as well as the speed‐up achieved compared with intrusive hyper‐reduced‐order approaches.

Water quality and macrophytes in the Danube River: Artificial neural network modelling

Ecological assessment of large rivers such as the Danube is a challenging task. Eutrophication was reported as one of the main drivers that structure aquatic communities in the Danube basin. Due to their sedentary nature, relatively slow growth/ long life spans, and engineering role in aquatic ecosystems, macrophytes are widely used in the detection of nutrient enrichment. In this study, macrophyte presence-absence data within the 3 km long reaches obtained from the Joint Danube Survey (JDS3) were used to predict the water quality of the Danube river and its main tributaries. For each water quality variable (dissolved oxygen, nitrate-nitrogen, and orthophosphates), a multi-layer feed-forward artificial neural network model (ANN) was constructed using the macrophytes as explanatory variables. Despite the limited number of samples (123) along the wide trophic gradient of the Danube, the model showed good predictive performances for the main river channel. The highest discrepancy between observed and predicted water quality was obtained for the samples collected in the tributaries or downstream from the tributaries' mouth, where the model predicted better trophic conditions compared to measured ones. From 64 analysed macrophyte species, 28 were selected by sensitivity analysis as key water quality indicators (KIS) for at least one environmental variable. KIS mainly belonged to the eutrophic tolerant submerged or emerged species with broad ecological amplitude, which reflects the significance of the developed model for use on rivers subjected to nutrient pollution. However, the use of the developed predictive model is restricted to the river sections having a water velocity suitable for macrophytes growth. The developed ANN architecture represents the modelling approach which could be applied to other lotic systems and biological quality elements.

Modeling LWR fuel Rod's gap thickness heat transfer coefficient by artificial neural network technique

During the lifetime of nuclear Light Water Reactors (LWRs), the fuel-cladding gap thickness heat transfer coefficient (hgap) is the most crucial parameter of fuel rod performance that determines the fuel rod thermo-mechanical behavior as well as the maximum level of fuel burn-up during the normal operation. The main objective of the present work is to develop a smart calculative method by using the artificial neural network (ANN) technique to predict the hgap for a fuel rod of LWRs. The parameters of ANN input include those of fuel design gap initial thickness and pressure, operation time, Linear Heat Generation (LHG), and the average fuel burn-up while hgap is considered as the only output parameter. The obtained results of FRAPCON-3.5 steady-state fuel rod performance code is used to form the training data set. A Multi-Layer Perceptron feed-forward ANN with one hidden layer is trained with the Levenberg–Marquardt training algorithm. It has been illustrated that the created artificial network can accurately predict the hgap. Through the weight matrix of the ANN, Garson's sensitive analysis procedure shows the significant role of fuel burn-up in determining the level of hgap.

Thermoeconomic modeling and artificial neural network optimization of Afyon geothermal power plant

The Afyon Geothermal Power Plant is modeled using the Multi-Layer Feed-Forward Artificial Neural Network. The 100 × 8 data set obtained from the real Binary Geothermal Power Plant is divided into two parts: 80 × 8 training data and 20 × 8 test data. Geothermal Power Plant system modeling has been performed numerically on Matlab with three inputs and five outputs. There are ten neurons in the hidden layer in the Artificial Neural Network-based system, and the logarithmic sigmoid transfer function is used as the transfer function in each neuron. The neurons in the output layer have the purelin transfer function. As a result of the training process, the 3.06 × 10E-2 mean square error value was obtained from the ANN-based Binary Geothermal Power Plant system. The main point of the study is the optimization of the binary geothermal power plant. The genetic algorithm method with Artificial Neural Network-based is used for this purpose. The results obtained from the outputs of the Artificial Neural Network-based Binary Geothermal Power Plant system are presented. The plant’s geothermal water temperature and mass flow rates are 110 °C and 150 kg/s. Energy and exergy efficiencies of the plant are calculated as 10.4% and 29.7%. The optimized simple payback period and exergy cost of the electricity generated in the plant is calculated as 2.87 years and 0.0176 $/kWh, respectively.

Inverter-Data-Driven Second-Level Power Forecasting for Photovoltaic Power Plant

Globally, the installed capacity of photovoltaic (PV) power plants is undergoing rapid growth. However, the random output power fluctuation of PV plants has brought great challenge to power system stable operation. One of the most effective approaches to counteract the impact of power fluctuation is to carry out power forecasting under ultra-short-term scales, among which the second-level power forecasting technique is still absent since the pure historical data analysis of PV plant output power cannot deal with the sudden output power change induced by moving cloud and the traditional cloud image detection methods actually fail to achieve accurate PV power forecasting in high temporal and spatial resolutions. This article proposes an inverter-data-driven method to achieve the second-level PV power forecasting. In specific, multilayer feed-forward artificial neural network based on the error back propagation algorithm is applied to first establish the mapping relations from shading conditions of PV array to its corresponding output power. Combining with output power of neighboring PV arrays as well as their geographical location information, the shading conditions of those shaded PV array can be reversely deduced. And then, the deduced shading conditions of all PV arrays will be converted into one virtual cloud image to identify the instant cloud characteristics. In consequence, the consecutively generated virtual cloud images can depict the moving direction, speed, and shape of shading cloud and then help easily realize the second-level power forecasting of PV plant. Case study and experimental results verified the performance of the proposed method.

Design and implementation of hydrogen economy using artificial neural network on field programmable gate array

In this study, economic analysis of the hydrogen generation and liquefaction system has been modeled using Multi-Layer Feed-Forward Artificial Neural Network (MLFFANN) and implemented on Field Programmable Gate Array (FPGA). Firstly, the 100X6 data set has been created to be used in the ANN-based modeling of the system using the Engineering Equation Solver (EES) program. This data set has been divided into two data sets as 80X6 for training and 20X6 for testing. The structure of the ANN-based economic analysis of hydrogen generation and liquefaction has been composed of 3 neurons in the input layer, ten neurons in the hidden layer, and three neurons in the output layer. Elliott-2-based TanSig transfer function and Purelin transfer function have been used in the neurons of the hidden layer and the output layer, respectively. Then, the ANN-model has been trained and tested using the Matlab program. The MSE values, 1.40x10E-7 and 2.07x10E-5, have been obtained as the results of the training phase and test phase of the ANN-based system, respectively. After getting fruitful results from training and testing phases, the economic analyses of hydrogen generation and liquation systems have been modeled in VHDL using bias and weight values located in the constructed ANN-based system using Matlab. The modeling has been performed in the Xilinx ISE Design Tools program using a 32-bit IEEE-754-1985 floating-point number standard. Then, the modeled ANN-based economic analysis of the hydrogen generation and liquation system has been implemented on the Xilinx Virtex-7 FPGA chip by performing the Place&Route process. The maximum operating frequency of the ANN-based hydrogen generation and liquefaction economy system implemented on FPGA has been obtained as 281.702 MHz using Xilinx ISE Design Tools.

Artificial Neural Network-Based 4-D Hyper-Chaotic System on Field Programmable Gate Array

In this presented study, a 4-D hyper-chaotic system newly proposed to the literature, has been implemented as Multi-Layer Feed-Forward Artificial Neural Network-based on FPGA chip with 32-bit IEEE-754-1985 floating-point number standard to be utilized in real time chaos-based applications. In the first step of the study, 4-D hyper-chaotic system has been numerically modeled on FPGA using Dormand-Prince numeric algorithm. In the second step, the data set (4X10,000) obtained from Matlab-based numeric model has been divided into two parts as training data set (4X8,000) and test data set (4X2,000) to create ANN-based 4-D hyper-chaotic system. A Multi-Layer Feed-Forward ANN structure with 4 inputs and 4 outputs has been constructed for ANN-based 4-D hyper-chaotic system. This structure has only one hidden layer and there are 8 neurons having Tangent Sigmoid activation function used as the activation function in each neuron. 2.58E-07 Mean Square Error (MSE) value has been obtained from the training of ANN-based 4-D hyper-chaotic system. In the third step, after the successful training of ANN-based 4-D hyper-chaotic system, the design of ANN-based 4-D hyper-chaotic system has been carried out on FPGA by taking the bias and weight values of the ANN structure as reference. In this step, at first, Matlab-based Feed-Forward Multi-Layer 4X8X4 network structure has been coded in Very High Speed Integrated Circuit Hardware Description Language (VHDL) to be implemented on FPGA chips. Then, the bias and weight values of the ANN structure has been converted from decimal number system to floating-point number standard and these converted values have been embedded into the network structure. In the last step, the ANN-based 4-D hyper-chaotic system designed on FPGA has been synthesized and tested using Xilinx ISE Design Suite. The chip statistics have been given after the Place&Route process carried out for the Virtex XC6VHX255T-3FF1155 FPGA chip. The maximum operating frequency of ANN-based 4-D hyper-chaotic system on FPGA has been obtained as 240.861 MHZ.

Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics.

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77–0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75–0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.

Prediction of electrical conductivity using ANN and MLR: a case study from Turkey

The study areas are located in Turkey (Kastamonu, Bartin, Karabuk, Sivas) and contain very different rock types, various mining and agricultural activity opportunities. So, the areas have groundwaters that have different chemical compositions and electrical conductivity (EC) values. The EC can be measured using EC meter, and it must be measured in situ. But, the measurement of EC in situ is laborious, time-consuming, expensive, and difficult in arduous terrain environments. In recent years, machine learning models have been a primary focus of interest for a lot of study by providing often highly accurate forecast for solutions of such problems. The aim of the study is to forecast EC of groundwater using artificial neural networks (ANN) and multiple linear regressions (MLR). Twelve different hydrochemical parameters, which affect the EC, such as major/minor ions and trace elements, were used in the analysis. Multilayer feed-forward ANN trained with backpropagation in Python machine learning libraries was used in this study. In order to obtain the most appropriate ANN architecture, trial-and-error procedure was used and different numbers of hidden layers, neurons, activation functions, optimizers, and test sizes were constructed. This study also tests the usability of input parameters in EC prediction studies. As a result, comparisons between the measured and predicted values indicated that the machine learning models could be successfully applied and provide high accuracy and reliability for EC and similar parameters forecasting.

Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation

The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and R 2 of 0.8254.

Application of Computational Intelligence in Describing the Drying Kinetics of Persimmon Fruit (Diospyros kaki) During Vacuum and Hot Air Drying Process

This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.

스테레오 시각의 3 차원 위치 측정을 위한 인공신경망 기법

This study proposes a three-dimensional (3D) position measurement method that uses two cameras fixed at arbitrary positions. Unlike many existing techniques, the proposed method allows a direct 3D position computation from the image coordinates. The direct image-to-space relations are redundantly obtained and can be used to make efficient 3D computations. The proposed model can be easily implemented using multilayer feedforward artificial neural networks due to directness. In our test, four small neural networks were constructed using different combinations of image coordinates as the network inputs. The most accurate network was selected. As a result, we could easily train the neural networks and obtain better results compared to existing methods.

A hybrid methodology using finite elements and neural networks for the analysis of adhesive anchors exposed to hurricanes and adverse environments

Hurricanes are responsible for approximately $28bn of damage every year in the United States alone, which may reach $151bn by 2075 due to the intensification of climate change according to certain prediction models. Approximately 35% of this damage is estimated to come from anchorage failures of non-structural components (NSCs). Severe exposure of NSCs to the adverse environments (such as elevated temperatures and long-term concrete cracking) and wind-induced bending effects during hurricanes promote anchorage failures. Three-dimensional (3D) nonlinear finite element (NLFE) analysis methods are currently required for simulating the anchor behavior due to the 3D phenomena involved; however, these models are rather complex and computationally prohibitive for analyzing large systems commonly encountered in practice. This study proposes a 2D analysis methodology that combines the strengths of 3D numerical modeling with the artificial neural network techniques to rapidly simulate the anchorage behavior while accounting for the effects of the adverse environmental exposure, concrete cone failure, and wind-induced bending effects. The methodology, which is validated with experimental data and 3D NLFE analyses, employs three distinct techniques as follows: (i) a novel modeling approach, ‘the Equivalent Cone Method,’ to accurately simulate the concrete cone breakout failure, (ii) analytical equations developed to account for wind-induced beam bending and elevated temperatures, and (iii) a multilayered feed-forward artificial neural network, trained and tested with the experimental data from a worldwide database, to rapidly account for long-term concrete cracking experienced by rooftop slabs. By employing these techniques, the proposed methodology permits the use of 2D NLFE models for anchor analysis with accuracies comparable to advanced 3D NLFE models but at a fraction of the computational cost.

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the \emph{a priori} settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs multilayer feedforward artificial neural network (ANN) with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network (CNN) fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. We detail the hyperparameters optimization of these models using the grid search algorithm.

SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity

SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity