A fuzzy neural network for web service selection aimed at dynamic software rejuvenation

Kalantari, Kimia Rezaei; Ebrahimnejad, Ali; Motameni, Homayun

doi:10.3906/elk-2001-33

Cited by 6 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The author (Rahi et al, 2023) has used C5.0 decision trees for the classification of faults. The author has used a fuzzy neural network technique for dynamic fault detection (Kalantari et al, 2020). For choosing an optimized subset of software requirements the authors (Pirozmand et al, 2021;Alrezaamiri et al, 2020) have used the Binary Artificial Algae and Parallel multi-objective artificial bee colony algorithms while (Alrezaamiri et al, 2019), the author has formulated a fuzzy multi-objective optimization problem.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptron

Mustaqeem,

Mustajab,

Alam

2024

IJICC

View full text Add to dashboard Cite

PurposeSoftware defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approachThe integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.FindingsThe performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.Originality/valueExperimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…, 2023) has used C5.0 decision trees for the classification of faults. The author has used a fuzzy neural network technique for dynamic fault detection (Kalantari et al. , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptron

Mustaqeem,

Mustajab,

Alam

2024

IJICC

View full text Add to dashboard Cite

show abstract

“…The estimate in the real system resorts to uncertain parameters with their predicted values and may result in a cautious out-put with unnecessarily high operating costs [57]. The stochastic approach was developed by Kalantari (2020) when highlighting the significance of deterministic meaning based on a fuzzy neural network for reducing the error in the searched optimal value [58]. A mathematical optimization enhances the metaheuristic searching by hybridizing with another artificial technique as discussed by Yang et al and Qiu et al [59,60].…”

Section: Stage (2): Predicting the Integration Efficiencymentioning

confidence: 99%

Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique

2022

View full text Add to dashboard Cite

Scholars seek to recycle wasted energy to produce electricity by integrating thermoelectric generators (TEGs) with internal combustion engines (ICE), which rely on the electrical conductivity, β, of the thermal conductor strips. The TEG legs are alloyed from iron, aluminum and copper in a strip shape with specific characteristics that guarantee maximum thermo-electric transformation, which has fluctuated between a uniform, Gaussian, and exponential distribution according to the structure of the alloy. The ICE exhaust and intake gates were chosen as the TEG sides. The digital simulator twin model checks the integration efficiency through two sequential stages, beginning with recording the causes of thermal conductivity failure via filming and extracting their data by neural network procedures in the feed of the second stage, which reveal that the cracks are a major obstacle in reducing the TEG-generated power. Therefore, the interest of the second stage is predicting the cracks’ positions, Pi,j, and their intensity, QP, based on the ant colony algorithm which recruits imaging data (STTF-NN-ACO) to install the thermal conductors far away from the cracks’ positions. The proposed metaheuristic (STTF-NN-ACO) verification shows superiority in the prediction over [Mat-ACO] by 8.2% and boosts the TEGs’ efficiency by 32.21%. Moreover, increasing the total generated power by 12.15% and working hours of TEG by 20.39%, reflects reduced fuel consumption by up to 19.63%.

show abstract

New Movement Strategy of Cuckoo Search for achieving higher profit rate in milling operation

Singh

Mohanty

2022

Results in Control and Optimization

View full text Add to dashboard Cite

A fuzzy neural network for web service selection aimed at dynamic software rejuvenation

Cited by 6 publications

References 28 publications

A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptron

A hybrid approach for optimizing software defect prediction using a gray wolf optimization and multilayer perceptron

Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique

New Movement Strategy of Cuckoo Search for achieving higher profit rate in milling operation

Contact Info

Product

Resources

About