scholarly journals Smart learning: A search-based approach to rank change and defect prone classes

2015 ◽  
Author(s):  
Carol V Alexandru ◽  
Annibale Panichella ◽  
Sebastiano Panichella ◽  
Alberto Bacchelli ◽  
Harald C Gall
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Regression Tree ◽  
Limited Resources ◽  
Smart Learning ◽  
Software Artifacts ◽  
The Right ◽  
Multiple Releases

Research has yielded approaches for predicting future changes and defects in software artifacts, based on historical information, helping developers in effectively allocating their (limited) resources. Developers are unlikely able to focus on all predicted software artifacts, hence the ordering of predictions is important for choosing the right artifacts to concentrate on. We propose using a Genetic Algorithm (GA) for tailoring prediction models to prioritize classes with more changes/defects. We evaluate the approach on two models, regression tree and linear regression, predicting changes/defects between multiple releases of eight open source projects. Our results show that regression models calibrated by GA significantly outperform their traditional counterparts, improving the ranking of classes with more changes/defects by up to 48%. In many cases the top 10% of predicted classes can contain up to twice as many changes or defects.

scholarly journals Smart learning: A search-based approach to rank change and defect prone classes

2015 ◽  
Author(s):  
Carol V Alexandru ◽  
Annibale Panichella ◽  
Sebastiano Panichella ◽  
Alberto Bacchelli ◽  
Harald C Gall
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Regression Tree ◽  
Limited Resources ◽  
Smart Learning ◽  
Software Artifacts ◽  
The Right ◽  
Multiple Releases

Research has yielded approaches for predicting future changes and defects in software artifacts, based on historical information, helping developers in effectively allocating their (limited) resources. Developers are unlikely able to focus on all predicted software artifacts, hence the ordering of predictions is important for choosing the right artifacts to concentrate on. We propose using a Genetic Algorithm (GA) for tailoring prediction models to prioritize classes with more changes/defects. We evaluate the approach on two models, regression tree and linear regression, predicting changes/defects between multiple releases of eight open source projects. Our results show that regression models calibrated by GA significantly outperform their traditional counterparts, improving the ranking of classes with more changes/defects by up to 48%. In many cases the top 10% of predicted classes can contain up to twice as many changes or defects.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

scholarly journals Ubiquitous Learning Environment for Smart Improving Disruption Activities in Classroom on Media

2020 ◽  
Vol 14 (13) ◽  
pp. 200 ◽  
Author(s):  
Muhammad Ashar ◽  
Harits Ar Rosyid ◽  
Agusta Rahmat Taufani ◽  
Khaidir Rahman Nasir
Keyword(s):  
Virtual Environments ◽  
Mobile Applications ◽  
Limited Resources ◽  
Ubiquitous Learning ◽  
Intelligent Environment ◽  
Smart Learning ◽  
Waterfall Model ◽  
Class Room ◽  
The Right ◽  
Ubiquitous Learning Environment

Smart learning emphasizes the educational needs of students learning to grow smarter as a result of an intelligent environment. Universities should have the right strategy through the utilization of limited resources, especially with regard to human resources. Objective research offers the benefits of learning in the form of influence over these resources are limited and focused on the use of online learning to support learning through class room facility improvements with a medium of learning through virtual environments and utilize technology and multimedia content adaftive cameras in some areas. The research method using descriptive experimental method in the fifth stage of design and product development of mobile applications in the form of a waterfall model through a needs analysis devices and digital facilities

scholarly journals Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

2017 ◽  
Vol 44 (12) ◽  
pp. 994-1004 ◽  
Author(s):  
Ivica Androjić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Linear Regression Models ◽  
Artificial Neural ◽  
Multiple Linear Regression Models

The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.

scholarly journals The Predictive Power of Regression Models to Determine Grass Weed Infestations in Cereals Based on Drone Imagery—Statistical and Practical Aspects

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2277
Author(s):  
Signe M. Jensen ◽  
Muhammad Javaid Akhter ◽  
Saiful Azim ◽  
Jesper Rasmussen
Keyword(s):  
Linear Regression ◽  
Random Sampling ◽  
Regression Models ◽  
Predictive Power ◽  
Prediction Models ◽  
Extreme Values ◽  
Learning Algorithm ◽  
Training Data ◽  
Growth Stages ◽  
Weed Detection

Site-specific weed management (SSWM) may reduce herbicide use by identifying weed patches and weed-free areas. However, one major constraint is robust weed detection algorithms that are able to predict weed infestations outside of the training data. This study investigates the predictive power of regression models trained on drone imagery that are used within fields to predict infestations of annual grass weeds in the late growth stages of cereals. The main objective was to identify the optimum sampling strategy for training regression models based on aerial RGB images. The study showed that training based on sampling from the whole range of weed infestations or the extreme values in the field provided better prediction accuracy than random sampling. Prediction models based on vegetation indices (VIs) offered a useful alternative to a more complex random forest machine-learning algorithm. For binary decision-making, linear regression utilizing weed density information resulted in higher accuracy than a logistic regression approach that only relied on information regarding the presence/absence of weeds. Across six fields, the average balanced accuracy based on linear regression was in the range of 75–83%, with the highest accuracy found when the sampling was from the extreme values in the field, and with the lowest accuracy found for random sampling. For future work on training weed prediction models, choosing training sets covering the entire sample space is recommended in favor of random sampling.

scholarly journals Correcting field determination of elemental contents in soils via portable X-ray fluorescence spectrometry

2020 ◽  
Vol 44 ◽  
Author(s):  
Thaís Santos Branco Dijair ◽  
Fernanda Magno Silva ◽  
Anita Fernanda dos Santos Teixeira ◽  
Sérgio Henrique Godinho Silva ◽  
Luiz Roberto Guimarães Guilherme ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Elemental Content ◽  
Auxiliary Variables ◽  
Linear Regression Models ◽  
X Ray ◽  
Laboratory Conditions ◽  
Elemental Contents ◽  
Field And Laboratory Conditions

ABSTRACT Portable X-ray fluorescence (pXRF) spectrometry has been useful worldwide for determining soil elemental content under both field and laboratory conditions. However, the field results are influenced by several factors, including soil moisture (M), soil texture (T) and soil organic matter (SOM). Thus, the objective of this work was to create linear mathematical models for conversion of Al2O3, CaO, Fe, K2O, SiO2, V, Ti and Zr contents obtained by pXRF directly in field to those obtained under laboratory conditions, i.e., in air-dried fine earth (ADFE), using M, T and SOM as auxiliary variables, since they influence pXRF results. pXRF analyses in field were performed on 12 soil profiles with different parent materials. From them, 59 samples were collected and also analyzed in the laboratory in ADFE. pXRF field data were used alone or combined to M, T and SOM data as auxiliary variables to create linear regression models to predict pXRF ADFE results. The models accuracy was assessed by the leave-one-out cross-validation method. Except for light-weight elements, field results underestimated the total elemental contents compared with ADFE. Prediction models including T presented higher accuracy to predict Al2O3, SiO2, V, Ti and Zr, while the prediction of Fe and K2O contents was insensitive to the addition of the auxiliary variables. The relative improvement (RI) in the prediction models were greater in predictions of SiO2 (T+SOM: RI=22.29%), V (M+T: RI=18.90%) and Ti (T+SOM: RI=11.18%). This study demonstrates it is possible to correct field pXRF data through linear regression models.

scholarly journals Generation of the bathymetry of a eutrophic shallow lake using WorldView-2 imagery

2013 ◽  
Vol 16 (1) ◽  
pp. 50-59 ◽  
Author(s):  
Onur Yuzugullu ◽  
Aysegul Aksoy
Keyword(s):  
Linear Regression ◽  
Shallow Lake ◽  
Regression Models ◽  
Prediction Models ◽  
Satellite Image ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Non Linear ◽  
Eutrophic Shallow Lake ◽  
Bathymetric Map

In this study, water depth distribution (bathymetric map) in a eutrophic shallow lake was determined using a WorldView-2 multispectral satellite image. Lake Eymir in Ankara (Turkey) was the study site. In order to generate the bathymetric map of the lake, image and data processing, and modelling were applied. First, the bands that would be used in depth prediction models were determined through statistical and multicollinearity analyses. Then, data screening was performed based on the standard deviation of standardized residuals (SD_SR) of depth values determined through preliminary linear regression models. This analysis indicated the sampling points utilized in depth modelling. Finally, linear and non-linear regression models were developed to predict the depths in Lake Eymir based on remotely sensed data. The non-linear regression model performed slightly better compared to the linear one in predicting the depths in Lake Eymir. Coefficients of determination (R2) up to 0.90 were achieved. In general, the bathymetric map was in agreement with observations except at re-suspension areas. Yet, regression models were successful in defining the shallow depths at shore, as well as at the inlet and outlet of the lake. Moreover, deeper locations were successfully identified.

scholarly journals Proposta de um modelo para predição do resultado das eleições presidenciais brasileiras baseado em técnicas de regressão

2021 ◽  
Author(s):  
Gabriel Robaina ◽  
Fabiano Baldo
Keyword(s):  
Linear Regression ◽  
Prediction Models ◽  
Regression Tree ◽  
Multivariate Linear Regression ◽  
Prediction Errors ◽  
Popular Vote ◽  
Wide Availability ◽  
Vote Intention ◽  
Tree Models ◽  
Political Economic

Prediction of elections is a subject that excites the population, especially in the last few months before an election. In Brazil, there is a wide availability of political, economic and social data, in institutions such as TSE, IBGE and opinion research institutes that can be used as sources to create prediction models. Therefore, this work aims to build multivariate linear regression and regression tree models to predict the percentage of votes received by the situational candidate for the presidency of Brazil. The multivariate linear regression model had the smallest prediction errors, with MAE of 1.45 in the first round and 1.48 in the second, with margins smaller than 1\% in 2002, 2006 and 2018. The proposed models seemed to be more accurate than other models found in the literature. As main contributions, it was possible to observe that the sampling of data by state and the use of the illiteracy rate and the popular vote intention contributed directly to the performance of the models.

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