scholarly journals Application of Machine Learning Techniques to Estimate Unsoaked California Bearing Ratio in Ekiti Central Senatorial District

2021 ◽  
pp. 30-37
Author(s):  
Akinwamide Joshua Tunbosun ◽  
Jacob Odeh Ehiorobo ◽  
Osuji Sylvester Obinna ◽  
Ebuka Nwankwo
Keyword(s):  
Machine Learning ◽  
Physical Properties ◽  
Information Criterion ◽  
Soil Physical Properties ◽  
Least Square ◽  
California Bearing Ratio ◽  
Machine Learning Techniques ◽  
Single Node ◽  
Error Metrics ◽  
Learning Techniques

This paper investigates the relationship between soil physical properties and the Un-soaked California Bearing Ratio (USCBR) of soil found in Ekiti State Central Senatorial District (ESCSD), which includes Natural Moisture Content (NMC%) Percentage Fines, Specific Gravity (SG) and Consistency Limits (LL%, PL%, & PI %). The database was prepared in the laboratory by conducting tests on ninety-nine (99) soil samples which were obtained in a burrowed pit found in the Central Senatorial District of Ekiti State. An R version 4.0.5 and R studio version 1.2.5033 was used to analyze the Artificial Neural Networks (ANNs) and Least Square Regression (LSR) in order to develop a simplified CBR model. In both models, independent layer containing six nodes (soil physical properties) and the dependent layer containing a single node (i.e. CBR) were taken. The descriptive analysis for training and testing was performed; boxplots of the variables were plotted and; sensitivity analysis was carried out. The capacity of the developed equation was evaluated in terms of error metrics MSE and RMSE. The analysis showed that both ANN and MLR models predicted CBR close to the laboratory value. However, the model without the percentage passing sieve 200 (MIC) is the best, having Akaike Information Criterion and Bayesian Information Criterion values of 614.1707 and 627.5754 respectively, from the error metrics analysis, the results showed that PL and LL are the most influential variable that affects the developed CBR model's output. From the foregoing its concluded that the study has shown a relationship between the CBR value of Ekiti Central Senatorial District soil and its basic soils properties using machine learning techniques, also the developed CBR model will be useful tool to Civil engineers, geotechnical engineers and construction industry within the study area particularly in their preliminary stage of their project.

Prediction of the Hardness of Cu-Ti-Co Alloy Using Machine Learning Techniques

2018 ◽  
Vol 777 ◽  
pp. 372-376 ◽  
Author(s):  
Shan Feng Fang
Keyword(s):  
Machine Learning ◽  
Copper Alloys ◽  
Least Square ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Forecasting Accuracy ◽  
Learning Techniques ◽  
Vector Machines ◽  
Forecasting Performance

Diverse machine learning approaches were employed to build regression models for predicting mechanical property of Cu-Ti-Co alloy. The forecasting performance of the least-square support vector machines (LSSVM) model has been compared with other artificial intelligence methods such as GRNN, RBF-PLS and RBFNN. The models were developed and validated utilizing a cross-validation (CV) procedure to improve the forecasting accuracy and generalization ability. The result demonstrates that the generalization performance of the new LSSVM is slightly better or superior to those acquired using GRNN, RBF-PLS and RBFNN. In future, it would be expected that the relatively new model based on machine learning is used as an especially helpful implement to accelerate materials design of copper alloys.

Machine learning to estimate surface roughness from satellite images

2021 ◽  
Author(s):  
Abhilash Singh ◽  
Kumar Gaurav
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Ensemble Learning ◽  
Satellite Images ◽  
Surface Soil ◽  
Information Criterion ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Landsat 8 ◽  
Learning Techniques

<p>Soil surface attributes (mainly surface roughness and soil moisture) play a critical role in land-atmosphere interaction and have several applications in agriculture, hydrology, meteorology, and climate change studies. This study explores the potential of different machine learning algorithms (Support Vector Regression (SVR), Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, and Boosting Ensemble Learning) to estimate the surface soil roughness from Synthetic Aperture Radar (SAR) and optical satellite images in an alluvial megafan of the Kosi River in northern India. In a field campaign during 10-21 December 2019, we measured the surface soil roughness at 78 different locations using a mechanical pin-meter. The average value of the in-situ surface roughness is 1.8 cm. Further, at these locations, we extract the multiple features (backscattering coefficients, incidence angle, Normalised Difference Vegetation Index, and surface elevation) from Sentinel-1 A/B, LANDSAT-8 and SRTM data. We then trained and evaluated (in 60:40 ratio) the performance of all the regression-based machine learning techniques. </p><p>We found that SVR method performs exceptionally well over other methods with (R= 0.74, RMSE=0.16 cm, and MSE=0.025 cm<sup>2</sup>). To ensure a fair selection of machine learning techniques, we have calculated some additional criteria that include Akaike’s Information Criterion (AIC), corrected AIC and Bayesian Information Criterion (BIC). On comparing, we observed that SVR exhibits the lowest values of AIC, corrected AIC and BIC amongst all other methods, indicating best goodness-of-fit.</p>

scholarly journals Phase Segmentation of X-Ray Computer Tomography Rock Images using Machine Learning Techniques: an Accuracy and Performance Study

2016 ◽  
Author(s):  
Swarup Chauhan ◽  
Wolfram Rühaak ◽  
Hauke Anbergen ◽  
Alen Kabdenov ◽  
Marcus Freise ◽  
...  
Keyword(s):  
Machine Learning ◽  
Least Square ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
Synthetic Sample ◽  
Selection Scheme ◽  
X Ray ◽  
Learning Techniques

Abstract. Performance and accuracy of machine learning techniques to segment rock grains, matrix and pore voxels, from a 3D volume of X-ray tomographic (XCT) grey-scale rock images was evaluated. The segmentation and classification capability of unsupervised (k-means, fuzzy c-means, self-organized maps), supervised (artificial neural networks, least square support vector machines) and ensemble classifiers (bragging and boosting) was tested using XCT images of Andesite volcanic rock, Berea sandstone, Rotliegend sandstone and a synthetic sample. The averaged porosity obtained for Andesite (0.15 ± 0.017), Barea sandstone (0.15 ± 0.02), Rotliegend sandstone (0.14 ± 0.08), synthetic sample (0.50 ± 0.13) is in very good agreement to the respective laboratory measurement data and varies by a factor of 0.2. The k-means algorithm is the fastest of all machine learning algorithms, whereas least square support vector machine is the most computationally expensive. Assessment of accuracy by entropy and purity values for unsupervised techniques; mean squared root error, receiver operational characteristics (to train the classification model) for supervised techniques; and 10-fold cross validation for the ensemble classifiers was performed. In general, the accuracy was found to be largely affected by the feature vector selection scheme. As it is always a trade-off between performance and accuracy, it is difficult to isolate one particular machine learning algorithm which is best suited for the complex phase segmentation problem. Therefore, our investigation provides parameters that can help selecting the appropriate machine learning techniques for phase segmentation.

scholarly journals Analytics of Batting First in Indian Premier League Twenty20 Cricket Matches

2021 ◽  
Author(s):  
Christopher Brydges
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Information Criterion ◽  
Machine Learning Techniques ◽  
Stepwise Logistic Regression ◽  
Learning Context ◽  
Coin Toss ◽  
Premier League ◽  
Learning Techniques ◽  
Forward Stepwise

Purpose: Use ball-by-ball data from the Indian Premier League cricket tournament and machine learning techniques to predict match outcomes based on events occurring in the first inning of a match.Approach: Twelve predictor variables were entered into machine learning models (forward stepwise logistic regression using Akaike’s Information Criterion (AIC); forward stepwise logistic regression using Bayesian Information Criterion (BIC); random forests; naïve Bayes classifier), with match outcome as the dependent variable. Findings: The AIC model reported the highest accuracy in both the training and test datasets (69.92% and 67.18%, respectively). This model contains total runs scored, winning the coin toss, and home-ground advantage as positive predictors, and number of balls with no runs scored and number of balls with one run scored as negative predictors. All four models found that total runs scored in an inning was the most important predictor of match outcome, and no model included number of wickets lost as a predictor, although there could be an indirect effect through total runs scored. Originality: This study is novel in that it used both pre-match variables (home-ground) advantage and real-time measures (e.g., how many runs were scored in the powerplay) in a machine learning context to classify match results. The results can be used to adapt in-game tactics to maximize advantages of batsmen in favorable contexts.

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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