A Comparative Study Of The Performance For Predicting

9 hours ago A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network Myeonghun Lee School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea

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6 hours ago A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network

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1 hours ago A Comparative Study of Predicting Student’s Performance by use of Data Mining Techniques 122 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 © Global Society of Scientific Research and Researchers http://asrjetsjournal.org/

Author: Aysha Ashraf, Sajid Anwer, Muhammad Gufran Khan
Publish Year: 2018

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4 hours ago Abstract—This paper provides a comparative study to evaluate the effectiveness of machine learning techniques in predicting fuel cell performance. Several methods applied in fuel cell prognostics are selected, including a neural network, an adaptive neuro-fuzzy inference system, and a particle filtering approach.

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2 hours ago A Comparative Study of Predicting Student’s Performance by use of Data Mining Techniques Aysha Ashraf National University of Computer & Emerging Sciences, Department of Computer Sciences, Pakistan Sajid Anwer National University of Computer & Emerging Sciences, Department of Computer Sciences, Pakistan

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1 hours ago A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash Authors Furqan Farooq 1 2 , Slawomir Czarnecki 3 , Pawel Niewiadomski 3 , Fahid Aslam 4 , Hisham Alabduljabbar 4 , Krzysztof Adam Ostrowski 2 , Klaudia Śliwa-Wieczorek 2 , Tomasz Nowobilski 3 , Seweryn Malazdrewicz 3 Affiliations

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7 hours ago This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future.,The authors are going to use many ANNs in this study including, five different long short-term …

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7 hours ago A Comparative Study of the RSM and ANN Models for Predicting Surface Roughness in Roller Burnishing x The performance of ANN models for predicting surface roughness was found to be better compared to RSM models in terms of the prediction accuracy both for the training and the testing data sets. The ANN has ability to capture a high degree

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7 hours ago A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks S.Chithraa S.R.R. SenthilKumarb K.Chinnarajuc F.Alfin Ashmitad https://doi.org/10.1016/j.conbuildmat.2016.03.214 Get rights and content Highlights •

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4 hours ago A comparative study of the performance of multiphase flow pipeline simulators in a hilly terrain pipeline P. Dhoorjaty; P. Dhoorjaty The models that do not predict such predominant slugging are less accurate than others in predicting the timing and volume of ramp-up slugs. It is argued that these deficiencies of model prediction result from

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3 hours ago In §2, key parameters relevant for comparing prediction algorithms are identified, and §3concerns performance comparison of the reported results from 18 prediction systems. The results of the comparison are discussed in §4and our conclusions are summarized in §5. 2. Algorithmic performance factors

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4 hours ago This study is intended to investigate the reliability of different incident duration prediction models for real time application with a view to contribute to the development of a decision aid tool within the incident management process context where rough incident duration estimates are currently provided by traffic operators or police on the basis of their skill and …

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7 hours ago A Comparative Analysis of Techniques for Predicting Student Performance +DQD% \GåRYVNi CSU and KD Lab Faculty of Informatics Masaryk University, Brno [email protected] ABSTRACT The problem of student final grade prediction in a particular course has recently been addressed using data minin g techniques.

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7 hours ago Another related study involved the RF ML algorithm to predict students' performance prediction and correctly classified 701 instances while only 297 instances were incorrectly classified and the

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4 hours ago View code. Biodegradability_Prediction_QSAR_GCN A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure−Activity Relationship Model vs the Graph Convolutional Network.

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Just Now A comparative study was performed on the performance of classification algorithms that are conventionally known to have high accuracy Bayesian networks, Random Forest, Regression, SMO, J48, Bayesian Networks, Perceptron classifiers and Logistic regression based on frequency of intake of the drugs crack, meth, heroin and ketamine. Data Mining and pattern recognition …

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Frequently Asked Questions

What are the comparative methods in machine learning?

Comparative methods are vital for such prediction methods to devise results for further studies and application. In the following table consists of all the classifiers and the different scores like Accuracy, Precision, Recall, F1-score, and Gini Coefficient.

Can multiple regression analysis predict the compressive strength of high performance concrete?

In this study, Multiple Regression Analysis (MRA) and Artificial Neural Network (ANN) models are constructed to predict the compressive strength of High Performance Concrete containing nano silica and copper slag as partial cement and fine aggregate replacement respectively.

Which machine learning algorithms are best for predictive analysis?

The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability.

Do explanatory variables predict the severity of motorcycle crashes?

Second to RF was IBk with slightly better performance metrics than J48. One of the objectives of this empirical research was to evaluate the relative importance of explanatory variables in predicting the severity of motorcycle crashes.

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