Software reuse analytics using integrated random forest and gradient boosting machine learning algorithm

Sandhu, Amandeep; Batth, Ranbir Singh

doi:10.1002/spe.2921

Cited by 33 publications

(19 citation statements)

References 26 publications

(50 reference statements)

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“…The RF algorithm is based on an ensemble of large, correlated decision trees and combines the decisions of individual trees to produce accurate, stable results [ 14 ]. We used Breiman and Cutler’s RF method with the tuning parameter of “mtry” (randomly selected predictors at each split).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-based Classifiers for the Prediction of Low Birth Weight

et al. 2023

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Objectives: Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The main objectives of this study were to compare four machine learning classifiers in the prediction of LBW and to determine the most important factors related to this phenomenon in Hamadan, Iran.Methods: We carried out a retrospective cross-sectional study on a dataset collected from Fatemieh Hospital in 2017 that included 741 mother-newborn pairs and 13 potential factors. Decision tree, random forest, artificial neural network, support vector machine, and logistic regression (LR) methods were used to predict LBW, with five evaluation criteria utilized to compare performance.Results: Our findings revealed a 7% prevalence of LBW. The average accuracy of all models was 87% or higher. The LR method provided a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy of 74%, 89%, 7.04%, 29%, and 88%, respectively. Using LR, gestational age, number of abortions, gravida, consanguinity, maternal age at delivery, and neonatal sex were determined to be the six most important variables associated with LBW.Conclusions: Our findings underscore the importance of facilitating timely diagnosis of causes of abortion, providing genetic counseling to consanguineous couples, and strengthening care before and during pregnancy (particularly for young mothers) to reduce LBW.

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Section: Methodsmentioning

confidence: 99%

Machine Learning-based Classifiers for the Prediction of Low Birth Weight

et al. 2023

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“…As explained earlier, the fitness of the model will be improved by the correction of the drawbacks of the previous classifiers connected in a chain of the ensemble tree. We also applied Grid-based ensemble method [70] to adjust some parameters for improving the fitness function in each iteration of the optimization process. The main contributions of the optimization model can be summarized as follows 1) A concept of grid dominance is introduced to compare individuals in both of the mating and environmental selection processes.…”

Section: Optimization Modelmentioning

confidence: 99%

“…We use grid as a structure to determine the location of individuals in the objective space, then the method will advise for the adaptability with the evolutionary population. In order to reach the optimum solution, we use gird dominance and grid difference in the grid structure to generate the optimization model [70]. Gird dominance can be defined in equation 1:…”

Section: Optimization Modelmentioning

confidence: 99%

“…In this paper, we aim to present the novel approach to video segmentation by using the optimization of segmentation parameters based on Grid-based ensemble method [62]. Our approach is the combination of random forest and gradient boosting decision tree [63], [64], [65], [66], [67], [68], [69], [70]. The system framework of our approach will be explained in section 2.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms

Pensiri¹,

Visutsak²

2022

Preprint

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Video segmentation is crucial in a variety of practical applications especially in computer visions. Most of recent works in video segmentation are focusing on Deep learning based video segmentation, there are rooms for improvement in respect of the evolutionary algorithms. This paper aims to propose the novel method to video segmentation by using the optimization of segmentation parameters based on ensemble-based random forest and gradient boosting decision tree. The experimental results show Pareto front of segmentation parameters (hue, brightness, luminance, and saturation). Our optimization model yields accuracy: 85% +/-8.85 % (micro average: 85.00 %), average class precision: 84.88%, and average class recall: 85%. We also show the video segmentation results based on our optimization method and compare our results with Kinect-based video segmentation.

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“…The complex relationship of error reporting observed by emergencies can be the location of identical error intelligences that represent up to 70% of intelligences in the box of error emergency structures, particularly aimed at open-source schemes with massive end client networks [6]. There can be two phases to recognizing duplicate error reports, and they address complex issues.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Deep Learning Model for Software Bug Detection and Classification

Sivapurnima¹,

Manjula²

2023

Computer Systems Science and Engineering

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Software is unavoidable in software development and maintenance. In literature, many methods are discussed which fails to achieve efficient software bug detection and classification. In this paper, efficient Adaptive Deep Learning Model (ADLM) is developed for automatic duplicate bug report detection and classification process. The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory (CRF-LSTM) and Dingo Optimizer (DO). In the CRF, the DO can be consumed to choose the efficient weight value in network. The proposed automatic bug report detection is proceeding with three stages like pre-processing, feature extraction in addition bug detection with classification. Initially, the bug report input dataset is gathered from the online source system. In the pre-processing phase, the unwanted information from the input data are removed by using cleaning text, convert data types and null value replacement. The pre-processed data is sent into the feature extraction phase. In the feature extraction phase, the four types of feature extraction method are utilized such as contextual, categorical, temporal and textual. Finally, the features are sent to the proposed ADLM for automatic duplication bug report detection and classification. The proposed methodology is proceeding with two phases such as training and testing phases. Based on the working process, the bugs are detected and classified from the input data. The projected technique is assessed by analyzing performance metrics such as accuracy, precision, Recall, F_Measure and kappa.

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Software reuse analytics using integrated random forest and gradient boosting machine learning algorithm

Cited by 33 publications

References 26 publications

Machine Learning-based Classifiers for the Prediction of Low Birth Weight

Machine Learning-based Classifiers for the Prediction of Low Birth Weight

Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms

Adaptive Deep Learning Model for Software Bug Detection and Classification

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