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Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

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Computers, Materials & Continua 2024年第3期78卷 3469-3487页

作者： Sureka Sivavelu Venkatesh Palanisamy School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

关键词： Software defect prediction feature selection nonparametric statistical Torgerson-Gower scaling technique quadratic censored regressive convolution deep neural network softstep activation function nelder-mead method

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Unsteady MHD Casson Nanofluid Flow Past an Exponentially Accelerated Vertical Plate:An Analytical Strategy

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 431-460页

作者： T.Aghalya R.Tamizharasi Department of Mathematics School of Advanced SciencesVellore Institute of TechnologyVelloreTamilnadu632014India

In this study,the characteristics of heat transfer on an unsteady magnetohydrodynamic(MHD)Casson nanofluid over an exponentially accelerated vertical porous plate with rotating effects were investigated.The flow was driven by the combined effects of the magnetic field,heat radiation,heat source/sink and chemical reaction.Copper oxide(CuO)and titanium oxide(TiO2)are acknowledged as nanoparticle materials.The nondimensional governing equations were subjected to the Laplace transformation technique to derive closed-form solutions.Graphical representations are provided to analyze how changes in physical parameters,such as the magnetic field,heat radiation,heat source/sink and chemical reaction,affect the velocity,temperature and concentration profiles.The computed values of skin friction,heat and mass transfer rates at the surface were tabulated for various sets of input parameters.It is perceived that there is a drop in temperature due to the rise in the heat source/sink and the Prandtl number.It should be noted that a boost in the thermal radiation parameter prompts an increase in temperature.An increase in the Prandtl number,heat source/sink parameter,time and a decrease in the thermal radiation parameter result in an increase in theNusselt number.The computed values of the skin friction,heat andmass transfer rates at the surface were tabulated for various values of the flow parameters.The present results were compared with those of previously published studies andwere found to be in excellent agreement.This research has practical applications in areas such as drug delivery,thermal medicine and cancer treatment.

关键词： Thermal radiation radiative flux nanofluid copper oxide titanium oxide accelerated plate

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2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

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Prediction of Bandwidth of Metamaterial Antenna Using Pearson Kernel-Based Techniques

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Computers, Materials & Continua 2024年第3期78卷 3449-3467页

作者： Sherly Alphonse S.Abinaya Sourabh Paul School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Electronics Engineering Vellore Institute of TechnologyChennai600127India

The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas.Antenna parameters have recently been predicted using machine learning algorithms in existing literature.Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters.The accuracy of the prediction will be primarily dependent on the model that is used.In this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard kernel.Along with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this work.The prediction results of the proposed work are better when compared to the existing models in the literature.

关键词： Antenna pearson optimization bandwidth metamaterial

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Benzydamine hydrochloride ameliorates ethanol-induced inflammation in RAW 264.7 macrophages by stabilizing redox homeostasis

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Asian Pacific Journal of Tropical Biomedicine 2024年第2期14卷 73-81,I0006-I0009页

作者： Tiasha Dasgupta Venkatraman Manickam Department of Biomedical Sciences School of Biosciences and TechnologyVellore Institute of TechnologyVellore 632014Tamil NaduIndia

Objective:To evaluate the protective effect of benzydamine hydrochloride against ethanol-induced oxidative stress and inflammation in RAW 264.7 macrophages.Methods:RAW 264.7 macrophages were treated with ethanol(100 mM)and benzydamine hydrochloride(7.5μM).The imflammatory status was confirmed by measuring pro-(TNF-αand IL-6)and anti-inflammatory(IL-10)cytokines through ELISA and RT-PCR assays.Reactive oxygen species generation and mitochondrial membrane potential were investigated to study the protective role of benzydamine hydrochloride against ethanol-induced oxidative stress.Apoptosis detection was also investigated using flow cytometry and acridine orange/ethidium bromide staining.Results:Benzydamine hydrochloride significantly decreased the secretion of TNF-αand IL-6,as well as the generation of reactive oxygen species inside the cells,thereby stabilizing the mitochondrial membrane potential and reducing DNA fragmentation.The ethanol-induced cellular necrosis was also reversed by the administration of benzydamine hydrochloride.Conclusions:Benzydamine hydrochloride ameliorates ethanol-induced cell apoptosis and inflammation in RAW macrophages.

关键词： Alcohol Benzydamine hydrochloride Inflammation Oxidative stress Apoptosis

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FEA-based Geometrical Modification of Switched Reluctance Motor for Radial Force Reduction

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Chinese Journal of Electrical Engineering 2024年第1期10卷 124-135页

作者： Rani S Jayapragash R School of Electrical Engineering Vellore Institute of TechnologyChennai 600127India

Switched reluctance motors(SRMs)are becoming increasingly popular in the automotive sector owing to their robust design.Moreover,SRMs are preferred particularly for EV applications owing to their fault tolerance,magnet-free structure,and high power/torque density.The main concerns of SRM compared to other machines include torque ripple and vibration.The primary cause of vibration is the radial force created by the SRM.A geometry-based modification of the SRM to reduce the radial force without significantly changing the average torque produced is proposed.The primary goal is to design a 4-phase,8/6 SRM with a lower radial force.Two possible geometrical alterations are proposed:one with square windows and the other with circular holes on the rotor core.The windows are sized and positioned to avoid flux saturation.General criteria are developed for the optimal window size and placement.Finite element analysis(FEA)modelling of the SRM is used to validate its performance.The FEA results are compared with the performance parameters obtained using the analytical method.Utilizes the multiphysics design tool ANSYS to obtain the natural frequencies and associated deformations through modal analysis.Compared to the conventional geometry,the radial force is significantly reduced by providing windows.

关键词： 8/6 SRM radial force modal analysis FEA analysis natural frequencies SRM with windows

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Facial Expression Recognition with High Response-Based Local Directional Pattern (HR-LDP) Network

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Computers, Materials & Continua 2024年第2期78卷 2067-2086页

作者： Sherly Alphonse Harshit Verma School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia

Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled situations.The use of Local Directional Patterns(LDP),which has good characteristics for emotion detection has yielded encouraging results.An innova-tive end-to-end learnable High Response-based Local Directional Pattern(HR-LDP)network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed work.By combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions,this network considerably minimizes the number of network parameters.The cost of the parameters in our fully linked layers is up to 64 times lesser than those in currently used deep learning-based detection algorithms.On seven well-known databases,including JAFFE,CK+,MMI,SFEW,OULU-CASIA and MUG,the recognition rates for seven-class facial expression recognition are 99.36%,99.2%,97.8%,60.4%,91.1%and 90.1%,respectively.The results demonstrate the advantage of the proposed work over cutting-edge techniques.

关键词： Emotion classification CNN network HR-LDP

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MDCN:Modified Dense Convolution Network Based Disease Classification in Mango Leaves

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Computers, Materials & Continua 2024年第2期78卷 2511-2533页

作者： Chirag Chandrashekar K.P.Vijayakumar K.Pradeep A.Balasundaram School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)ChennaiIndia

The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accurate diagnosis of the disorders is essential.Deep convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification tasks.However,it requires large training datasets and several parameters that need careful adjustment.The proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango leaves.This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques.The data loader also builds mini-batches for training the models to reduce training time.Finally,optimization approaches help increase the overall model’s efficiency and lower computing *** employed on the MangoLeafBD Dataset consists of a total of 4,000 images.Following the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.

关键词： Leaf disease detection deep convolutional neural networks transfer learning optimization MangoLeafBD Dataset

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MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

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Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

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U-Net Inspired Deep Neural Network-Based Smoke Plume Detection in Satellite Images

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Computers, Materials & Continua 2024年第4期79卷 779-799页

作者： Ananthakrishnan Balasundaram Ayesha Shaik Japmann Kaur Banga Aman Kumar Singh Centre for Cyber Physical Systems Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiTamil Nadu600127India

Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have beenidentified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions isessential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcingemission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrialsmoke plumes using freely accessible geo-satellite imagery. The existing systemhas so many lagging factors such aslimitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timelyresponse to industrial fires. In this work, the utilization of grayscale images is done instead of traditional colorimages for smoke plume detection. The dataset was trained through a ResNet-50 model for classification and aU-Net model for segmentation. The dataset consists of images gathered by European Space Agency’s Sentinel-2 satellite constellation from a selection of industrial sites. The acquired images predominantly capture scenesof industrial locations, some of which exhibit active smoke plume emissions. The performance of the abovementionedtechniques and models is represented by their accuracy and IOU (Intersection-over-Union) metric.The images are first trained on the basic RGB images where their respective classification using the ResNet-50model results in an accuracy of 94.4% and segmentation using the U-Net Model with an IOU metric of 0.5 andaccuracy of 94% which leads to the detection of exact patches where the smoke plume has occurred. This work hastrained the classification model on grayscale images achieving a good increase in accuracy of 96.4%.

关键词： Smoke plume ResNet-50 U-Net geo satellite images early warning global monitoring

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