Software Fault Prediction Using LSSVM with Different Kernel Functions

Kulamala, Vinod Kumar; Kumar, Lov; Mohapatra, Durga Prasad

doi:10.1007/s13369-021-05643-2

Cited by 11 publications

(1 citation statement)

References 15 publications

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“…The least squares support vector machine (LSSVM) (Kulamala et al, 2021; Yong et al, 2021) is a variation of the support vector machine. The support vector machine is based on supervised learning to classify two different types of sample points.…”

Section: Methodsmentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

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Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time‐domain features, 13 frequency‐domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray‐scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and of the classification model. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.Practical applicationsCracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identification combine with feature fusion. The features of time domain, frequency domain and Mel spectrogram were extracted from the effective sound signal, and the features were fused. The influence of three feature selection methods, three models and three optimization algorithms on walnut sound signal recognition was analyzed and compared. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, and the method proposed in this study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.

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Section: Methodsmentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

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Dynamic Viscosity of Polyethylene Glycol (PEG): Data Assessment, Sensitivity Analysis and Robust Modeling via Artificial Intelligence Methods

Altalbawy,

Al‐Hussainy,

Doshi

et al. 2024

Polymers for Advanced Techs

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Accurate determination of physio‐chemical properties of polyethylene glycol as a green non‐toxic solvent is vital for any chemical and energy process involving polyethylene glycol. Machine learning algorithms are known as robust methods for prediction purposes. This study put forward least squares support vector machine scheme optimized with either particle swarm optimization, genetic algorithm and coupled simulated annealing optimization methods to precisely carry out the prediction task of polyethylene glycol viscosity in terms of its influencing parameters of pressure, temperature and polyethylene glycol molecular weight using a dataset containing experimental pertinent values. An outlier detection algorithm is made use of to confirm the data reliability for model development. In addition, sensitivity study is done to ascertain the relative impacts of each input factor on polyethylene glycol viscosity. The results showed that least squares support vector machine optimized with coupled simulated annealing is the most accurate model for the PEG viscosity prediction task with coefficient of determination of 0.997, average absolute relative error percent of 0.653 and mean square error of 8.005 for all datapoints. In addition, it was found that unlike temperature, we observe an indirect correlation of pressure and polyethylene glycol molecular weight with the pertinent viscosity data. The robustness of the developed least squares support vector machine optimized with coupled simulated annealing is further approved as it outperforms a renowned correlation that is often used for general viscosity prediction.

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Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition

Yang,

Bian

2024

Software Testing Verif & Rel

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Accurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, data‐driven nonparametric models have gradually attracted increasing attention with outstanding prediction performance. However, we found that there may exist some prediction lags caused by the autocorrelation and nonstationarity of software fault data in the prediction of nonparametric models affecting their performance. To address this problem, we proposed an adaptive gated recurrent unit‐based encoder‐decoder model (ED‐GRU) with ensemble empirical mode decomposition (EEMD), effectively reducing prediction lags and performing accurate software fault number prediction. The autocorrelation and nonstationarity of fault data are first reduced by using first‐order difference and EEMD to clearly characterize the changing trend of the data. The frequency‐specific ED‐GRU networks are then combined to adaptively learn the nonlinear fluctuation trend of fault data under different frequency scales and obtain accurate prediction final results after aggregation. Experiments on eight public datasets showed that the proposed EEMD‐ED‐GRU‐PF model could effectively reduce the prediction lags and achieve the best prediction performance compared with four nonparametric and five parametric baseline methods in all datasets. Therefore, the proposed method can effectively and stably reduce the prediction lag to significantly improve the prediction accuracy. In this way, developers can accurately evaluate the current quality of the software and provide valuable guidance for software development and maintenance.

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Software Fault Prediction Using LSSVM with Different Kernel Functions

Cited by 11 publications

References 15 publications

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Dynamic Viscosity of Polyethylene Glycol (PEG): Data Assessment, Sensitivity Analysis and Robust Modeling via Artificial Intelligence Methods

Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition

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