scholarly journals Predicting the Functional Impact of KCNQ1 Variants with Artificial Neural Networks

2021 ◽  
Author(s):  
Saksham Phul ◽  
Georg Kuenze ◽  
Carlos G. Vanoye ◽  
Charles R. Sanders ◽  
Alfred L. George ◽  
...  
Keyword(s):  
Treatment Decision ◽  
Heart Rhythm ◽  
Ann Model ◽  
Functional Impact ◽  
Functional Parameters ◽  
Evolutionary Features ◽  
Uncertain Significance ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
And Function

Predicting the functional impact of KCNQ1 variants of uncertain significance (VUS) can assist physicians in taking appropriate treatment decision for patients with genetic heart rhythm disorder. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1, that performs better or comparable with algorithms only on evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of evolutionary features in predicting variant activation voltage and deactivation time. As AI is increasing applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

2019 ◽  
Vol 28 (1) ◽  
pp. 35 ◽  
Author(s):  
Pablo Pozzobon de Bem ◽  
Osmar Abílio de Carvalho Júnior ◽  
Eraldo Aparecido Trondoli Matricardi ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Federal District ◽  
Burned Area ◽  
Ann Model ◽  
Wildfire Management ◽  
Explanatory Variables ◽  
Burned Areas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.

scholarly journals Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

scholarly journals Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

2021 ◽  
Vol 5 (2) ◽  
pp. 109-118
Author(s):  
Euis Saraswati ◽  
Yuyun Umaidah ◽  
Apriade Voutama
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Artificial Neural Network ◽  
Text Mining ◽  
Ann Model ◽  
Rapid Spread ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
The Government ◽  
Artificial Neural Network Ann

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.

scholarly journals Artificial Neural Network Trained to Predict High-Harmonic Flux

2018 ◽  
Author(s):  
Ana Maria Mihaela Gherman ◽  
Katalin Kovács ◽  
Mircea Vasile Cristea ◽  
Valer Tosa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Harmonic ◽  
Ann Model ◽  
Gas Cell ◽  
Dimensional Parameter ◽  
High Order Harmonic ◽  
Cell Position ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work we present the results obtained with an artificial neural network (ANN) which we trained to predict the expected output of high-order harmonic generation (HHG) process, while exploring a multi-dimensional parameter space. We argue on the utility and efficiency of the ANN model and demonstrate its ability to predict the outcome of HHG simulations. In this case study we present the results for a loose focusing HHG beamline, where the changing parameters are: the laser pulse energy, gas pressure, gas cell position relative to focus and gas cell length. The physical quantity which we predict here using ANN is directly related to the total harmonic yield in a specified spectral domain (20-40 eV). We discuss the versatility and adaptability of the presented method.

scholarly journals A Study on Correlation of Subjects on Electrical Engineering Course Using Artificial Neural Network (ANN)

2021 ◽  
Vol 17 (2) ◽  
pp. 144
Author(s):  
Fathiah Zakaria ◽  
Siti Aishah Che Kar ◽  
Rina Abdullah ◽  
Syila Izawana Ismail ◽  
Nur Idawati Md Enzai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Engineering ◽  
Electric Circuit ◽  
Time Correlation ◽  
Ann Model ◽  
Core Subject ◽  
History Of ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract: This paper presents a study of correlation between subjects of Diploma in Electrical Engineering (Electronics/Power) at Universiti Teknologi MARA(UiTM) Cawangan Terengganu using Artificial Neural Network (ANN). The analysis was done to see the effect of mathematical subjects (Pre-calculus and Calculus 1) and core subject (Electric Circuit 1) on Electronics 1. Electronics 1 is found to be a core subject with the history of high failure rate percentage (more than 25%) in previous semesters. This research has been conducted on current final semester students (Semester 5). Seven (7) models of ANN are developed to observe the correlation between the subjects. In order to develop an ANN model, ANN design and parameters need to be chosen to find the best model. In this study, historical data from students’ database were used for training and testing purpose. Total number of datasets used are 58 sets. 70% of the datasets are used for training process and 30% of the datasets are used for testing process. The Regression Coefficient, (R) values from the developed models was observed and analyzed to see the effect of the subject on the performance of students. It can be proven that Electric Circuit 1 has significant correlation with the Electronics 1 subject respected to the highest R value obtained (0.8100). The result obtained proves that student’s understanding on Electric Circuit 1 subject (taken during semester 2) has direct impact on the performance of students on Electronics 1 subject (taken during semester 3). Hence, early preventive measures could be taken by the respective parties.    Keywords: Artificial neural network, Diploma in Electrical Engineering, Graduate on time, Correlation.

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