scholarly journals An expert system for predicting the infiltration characteristics

2021 ◽  
Author(s):  
Balraj Singh ◽  
Isa Ebtehaj ◽  
Parveen Sihag ◽  
Hossein Bonakdari
Keyword(s):  
Gene Expression Programming ◽  
Infiltration Rate ◽  
Machine Learning Techniques ◽  
Subsurface Water ◽  
Single Factor ◽  
Learning Techniques ◽  
Cumulative Infiltration ◽  
Soft Computing Techniques ◽  
Water Quality And Quantity ◽  
Amp Clay

Abstract Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. This study includes a comparative analysis of the two machine learning techniques; M5P model tree (M5P) and Gene Expression Programming (GEP) in predictions of the infiltration characteristics. The models were trained and tested using the 7 combination (CMB1 – CMB7) of input parameters; moisture content (m), bulk density of soil (D), percentage of the silt (SI), sand (SA) & Clay (C), and time (t), with output parameters; cumulative infiltration (CI) and infiltration rate (IR). Results suggested that GEP has an edge over M5P to predict the IR and CI with R, RMSE & MAE values 0.9343, 15.9667 mm/hr & 8.7676 mm/hr, and 0.9586, 9.2522 mm and 7.7865 mm for IR and CI, respectively with CMB1. Although the M5P model also gave good results with R, RMSE & MAE values 0.9192, 14.1821 mm/hr, & 19.2497 mm/hr, and 0.8987, 11.2144 mm & 18.4328 mm for IR and CI, respectively, but lower than GEP. Furthermore, single-factor ANOVA and uncertainty analysis were used to show the significance of the predicted results and to find the most efficient soft computing techniques respectively.

scholarly journals Geomatics and Soft Computing Techniques for Infrastructural Monitoring

2020 ◽  
Vol 12 (4) ◽  
pp. 1606 ◽  
Author(s):  
Vincenzo Barrile ◽  
Antonino Fotia ◽  
Giovanni Leonardi ◽  
Raffaele Pucinotti
Keyword(s):  
Soft Computing ◽  
Structural Model ◽  
Control Unit ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Risk Level ◽  
Behavior Monitoring ◽  
Learning Techniques ◽  
Soft Computing Techniques ◽  
The One

Structural Health Monitoring (SHM) allows us to have information about the structure under investigation and thus to create analytical models for the assessment of its state or structural behavior. Exceeded a predetermined danger threshold, the possibility of an early warning would allow us, on the one hand, to suspend risky activities and, on the other, to reduce maintenance costs. The system proposed in this paper represents an integration of multiple traditional systems that integrate data of a different nature (used in the preventive phase to define the various behavior scenarios on the structural model), and then reworking them through machine learning techniques, in order to obtain values to compare with limit thresholds. The risk level depends on several variables, specifically, the paper wants to evaluate the possibility of predicting the structure behavior monitoring only displacement data, transmitted through an experimental transmission control unit. In order to monitor and to make our cities more “sustainable”, the paper describes some tests on road infrastructure, in this contest through the combination of geomatics techniques and soft computing.

scholarly journals Assessment and Analysis of Software Reliability Using Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 201
Author(s):  
G Krishna Mohan ◽  
N Yoshitha ◽  
M L.N.Lavanya ◽  
A Krishna Priya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Software Reliability ◽  
Additive Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Software Failure ◽  
Software Reliability Models ◽  
Learning Techniques ◽  
Soft Computing Techniques

Software reliability models access the reliability by fault prediction. Reliability is a real world phenomenon with many associated real time problems and to obtain solutions to problems quickly, accurately and acceptably a large no. of soft computing techniques has been developed. We attempt to address the software failure problems by modeling software failure data using the machine learning techniques such as support vector machine (SVM) regression and generalized additive models. The study of software reliability can be categorized into three parts: modeling, measurement, improvement. Programming unwavering quality demonstrating has developed to a point that important outcomes can be acquired by applying appropriate models to the issue; there is no single model all inclusive to every one of the circumstances. We propose different machine learning methods for the evaluation of programming unwavering quality, for example, artificial neural networks, support vector machine calculation approached. We at that point break down the outcomes from machine getting the hang of demonstrating, and contrast them with that of some summed up direct displaying procedures that are proportional to programming dependability models.  

scholarly journals Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 794 ◽  
Author(s):  
Ayaz Ahmad ◽  
Furqan Farooq ◽  
Pawel Niewiadomski ◽  
Krzysztof Ostrowski ◽  
Arslan Akbar ◽  
...  
Keyword(s):  
Fly Ash ◽  
Decision Tree ◽  
Mean Square Error ◽  
Gene Expression Programming ◽  
Model Performance ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Mean Square ◽  
Learning Techniques ◽  
Moderate Bias

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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