scholarly journals Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

2021 ◽  
Vol 3 (2) ◽  
pp. 182-198
Author(s):  
Kiran Kumar Paidipati ◽  
Christophe Chesneau ◽  
B. M. Nayana ◽  
Kolla Rohith Kumar ◽  
Kalpana Polisetty ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Rice Yield ◽  
Absolute Error ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
State Governments ◽  
Learning Techniques ◽  
Regression Approach ◽  
Rice Yields

The prediction of rice yields plays a major role in reducing food security problems in India and also suggests that government agencies manage the over or under situations of production. Advanced machine learning techniques are playing a vital role in the accurate prediction of rice yields in dealing with nonlinear complex situations instead of traditional statistical methods. In the present study, the researchers made an attempt to predict the rice yield through support vector regression (SVR) models with various kernels (linear, polynomial, and radial basis function) for India overall and the top five rice producing states by considering influence parameters, such as the area under cultivation and production, as independent variables for the years 1962–2018. The best-fitted models were chosen based on the cross-validation and hyperparameter optimization of various kernel parameters. The root-mean-square error (RMSE) and mean absolute error (MAE) were calculated for the training and testing datasets. The results revealed that SVR with various kernels fitted to India overall, as well as the major rice producing states, would explore the nonlinear patterns to understand the precise situations of yield prediction. This study will be helpful for farmers as well as the central and state governments for estimating rice yield in advance with optimal resources.

scholarly journals Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lasini Wickramasinghe ◽  
Rukmal Weliwatta ◽  
Piyal Ekanayake ◽  
Jeevani Jayasinghe
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Rice Yield ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Climatic Data ◽  
Climate Variables ◽  
Squared Error ◽  
Learning Techniques ◽  
The Relationship

This paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). The performance of each model was assessed in terms of the mean squared error (MSE), correlation coefficient (R), mean absolute percentage error (MAPE), root mean squared error ratio (RSR), BIAS value, and the Nash number, and it was found that the GPR-based model is the most accurate among them. Climate data collected until early 2019 (Maha season of year 2018) were used to develop the model, and an independent validation was performed by applying data of the Yala season of year 2019. The developed model can be used to forecast the future rice yield with very high accuracy.

Machine Learning Techniques for Estimating Amount of Coolant Required in Shipping of Temperature Sensitive Products

2020 ◽  
Vol 10 (10) ◽  
pp. 67-70
Author(s):  
Divya Choudhary ◽  
Siripong Malasri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Threshold Temperature ◽  
Support Vector ◽  
Temperature Sensitive ◽  
Learning Techniques ◽  
The Neural Networks

This paper implements and compares machine learning algorithms to predict the amount of coolant required during transportation of temperature sensitive products. The machine learning models use trip duration, product threshold temperature and ambient temperature as the independent variables to predict the weight of gel packs need to keep the temperature of the product below its threshold temperature value. The weight of the gel packs can be translated to number of gel packs required. Regression using Neural Networks, Support Vector Regression, Gradient Boosted Regression and Elastic Net Regression are compared. The Neural Networks based model performs the best in terms of its mean absolute error value and r-squared values. A Neural Network model is then deployed on as webservice to score allowing for client application to make rest calls to estimate gel pack weights

scholarly journals The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression

2021 ◽  
pp. 69-76
Author(s):  
William Mounter ◽  
Huda Dawood ◽  
Nashwan Dawood
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Energy Use ◽  
Building Energy ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Energy Usage ◽  
Energy Prediction ◽  
Learning Techniques

AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.

scholarly journals PREDICTION OF GLUCOSE LEVEL IN DIABETICS WITH SUPPORT VECTOR REGRESSION

2020 ◽  
Vol 16 (1) ◽  
pp. 97-102
Author(s):  
Devi Wulandari ◽  
Agus Subekti
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Mean Squared Error ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Glucose Levels ◽  
Existing Problems ◽  
Learning Techniques ◽  
Comparison Algorithms ◽  
The Times

One of the common diabetes factors that people hear is that they consume too much or often consume sweet foods or drinks so that blood sugar in the human body increases. The times and increasingly sophisticated technology make it easier for someone to be able to predict a disease such as diabetes with machine learning techniques. Therefore, from the existing problems, a machine learning technique will be made in predicting glucose levels in diabetics. The aim is to predict glucose levels in diabetics and find the best algorithm from several comparison algorithms. The results of the experiments carried out by the support vector regression algorithm have a lower mean squared error value of 28.9480 compared to other comparative algorithms and visualize the error classification seen that Instance no 47 has a prediction of the highest plasma glucose value of 189.2305.

scholarly journals Prediction of Glucose Concentration in Blood Plasma with Support Vector Regression Algorithm

2019 ◽  
Vol 8 (6S) ◽  
pp. 352-355
Keyword(s):  
Support Vector Regression ◽  
Diabetes Management ◽  
Performance Measure ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Sets ◽  
Glucose Levels ◽  
Learning Techniques ◽  
And Performance ◽  
Near Future

Diabetes Mellitus is due to the disorder of glucose metabolism because of defects in insulin secretion or insulin action. It has become a major health challenge nowadays. Monitoring and regulation of blood glucose is inevitable to avoid diabetic complications. Prediction of near future glucose levels and giving alert for appropriate action could be done by machine learning techniques. This would greatly assist the diabetes patients in the daily management of diabetes. This paper discusses the effectiveness of Support Vector Regression in diabetes management. The methodology has been applied to three different data sets and performance measure is analyzed with Root Mean Square Error values.

scholarly journals Covariance matrix forecasting using support vector regression

2021 ◽  
Author(s):  
Piotr Fiszeder ◽  
Witold Orzeszko
Keyword(s):  
Support Vector Regression ◽  
Covariance Matrix ◽  
Time Series Prediction ◽  
Dynamic Modelling ◽  
Positive Definiteness ◽  
Covariance Matrices ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Modelling And Forecasting

AbstractSupport vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that it can describe the dynamic characteristics of financial processes and make more accurate forecasts than other machine learning techniques. The first main contribution of this paper is to propose a methodology for dynamic modelling and forecasting covariance matrices based on support vector regression using the Cholesky decomposition. The procedure is applied to range-based covariance matrices of returns, which are estimated on the basis of low and high prices. Such prices are most often available with closing prices for many financial series and contain more information about volatility and relationships between returns. The methodology guarantees the positive definiteness of the forecasted covariance matrices and is flexible, as it can be applied to different dependence patterns. The second contribution of the paper is to show with an example of the exchange rates from the forex market that the covariance matrix forecasts calculated using the proposed approach are more accurate than the forecasts from the benchmark dynamic conditional correlation model. The advantage of the suggested procedure is higher during turbulent periods, i.e., when forecasting is the most difficult and accurate forecasts matter most.

scholarly journals Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7034
Author(s):  
Yue Xu ◽  
Waqas Ahmad ◽  
Ayaz Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
Marta Dudek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Support Vector Regression ◽  
High Performance ◽  
Cross Validation ◽  
High Performance Concrete ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Fold Cross Validation

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.

Air Pollution Forecasting Model Based on Chance Theory and Intelligent Techniques

2017 ◽  
Vol 26 (06) ◽  
pp. 1750024 ◽  
Author(s):  
Nabil Mohamed Eldakhly ◽  
Magdy Aboul-Ela ◽  
Areeg Abdalla
Keyword(s):  
Support Vector Regression ◽  
Air Pollutant ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Forecasting Model ◽  
Chance Theory ◽  
Novel Approach ◽  
Learning Techniques ◽  
Greater Cairo ◽  
Hourly Concentration

A novel approach of weighted support vector regression (WSVR) technique with applied chance theory was proposed to build a robust forecasting model, called the chance weighted support vector regression (chWSVR) model. In order to forecast the particulate matter air pollutant of diameter less than 10 micrometers (PM10) one hour advance in the Greater Cairo Metropolitan Area (GCMA) in Egypt. The chance theory has advanced concepts pertinent to treat cases where both randomness and fuzziness play simultaneous roles at one time. The basic idea based on the proposed chWSVR model is assigning the chance weight value of the target variable, based on the chance theory, to its corresponding dataset point to become minimized in the objective function making that point more significant during the training process. Measuring data were collected and reprocessed from four monitoring stations located in GCMA and relative to the springs during the period from 2007 to 2010. The results of such model compared to similar ones built by other machine learning techniques, Random Forest and Bootstrap aggregating techniques. In all stations, comparing such models revealed that the proposed chWSVR model findings were promising in the forecasting of PM10 hourly concentration.

Machine Learning Techniques for Identifying Fetal Risk During Pregnancy

2021 ◽  
Author(s):  
S. Ravikumar ◽  
E. Kannan
Keyword(s):  
Machine Learning ◽  
Fetal Heart ◽  
Quantitative Description ◽  
Absolute Error ◽  
Outcome Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Fetal Risk ◽  
Learning Techniques

Cardiotocography (CTG) is a biophysical method for assessing fetal condition that primarily relies on the recording and automated analysis of fetal heart activity. The quantitative description of the CTG signals is provided by computerized fetal monitoring systems. Even though effective conclusion generation methods for decision process support are still required to find out the fetal risk such as premature embryo, this proposed method and outcome data can confirm the assessment of the fetal state after birth. Low birth weight is quite possibly the main attribute that significantly depicts an unusual fetal result. These expectations are assessed in a constant experimental decision support system, providing valuable information that can be used to obtain additional information about the fetal state using machine learning techniques. The advancements in modern obstetric practice enabled the use of numerous reliable and robust machine learning approaches in classifying fetal heart rate signals. The Naïve Bayes (NB) classifier, support vector machine (SVM), decision trees (DT), and random forest (RF) are used in the proposed method. To assess these outcomes in the proposed method, some of the metrics such as precision, accuracy, F1 score, recall, sensitivity, logarithmic loss and mean absolute error have been taken. The above mentioned metrics will be helpful to predict the fetal risk.

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