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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 sta

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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Optimization for sound insulation of a sandwich plate with a corrugation and auxetic honeycomb hybrid core

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Applied Mathematics and Mechanics(English Edition) 2024年第9期45卷 1595-1612页

作者： Fenglian LI Yiping WANG Yuxing ZOU school of Mechanical and Electrical engineering Beijing Information Science and Technology UniversityBeijing 100192China

A sandwich plate with a corrugation and auxetic honeycomb hybrid core is constructed,and its sound insulation and optimization are ***,the motion governing equation of the sandwich plate is established by the third-order shear deformation theory(TSDT),and then combined with the fluid-structure coupling conditions,and the sound insulation is *** theoretical results are validated by COMSOL simulation results,and the effects of the structural parameter on the sound insulation are ***,the standard genetic algorithm is adopted to optimize the sound insulation of the sandwich plate.

关键词： optimization sound insulation hybrid core layer genetic algorithm

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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 *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** 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 ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** 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 *** improves the privacy of the local *** analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** 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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Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

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Defence Technology（防务技术） 2024年第5期35卷 275-288页

作者： Hancong Feng Kaili.Jiang Zhixing Zhou Yuxin Zhao Kailun Tian Haixin Yan Bin Tang school of information and Communication engineering University of Electronic Science and Technology of ChinaChengdu 611731China

The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic *** methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative *** paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse *** results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.

关键词： Probabilistic forecasting Multifunction radar Unsupervised learning Change point detection Outlier detection

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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Computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu school of Computer and information science Hubei Engineering UniversityXiaogan432000China school of Computer science and information engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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Federated Meta Reinforcement Learning for Personalized Tasks

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Tsinghua science and Technology 2024年第3期29卷 911-926页

作者： Wentao Liu Xiaolong Xu Jintao Wu Jielin Jiang school of Computer science Nanjing University of Information Science and TechnologyNanjing 210044China school of Software Nanjing University of Information Science and TechnologyNanjing 210044China

As an emerging privacy-preservation machine learning framework,Federated Learning(FL)facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and *** this learning framework is applied to Deep Reinforcement Learning(DRL),the resultant Federated Reinforcement Learning(FRL)can circumvent the heavy data sampling required in conventional DRL and benefit from diversified training data,besides privacy preservation offered by *** FRL implementations presuppose that clients have compatible tasks which a single global model can *** practice,however,clients usually have incompatible(different but still similar)personalized tasks,which we called task *** may severely hinder the implementation of FRL for practical *** this paper,we propose a Federated Meta Reinforcement Learning(FMRL)framework by integrating Model-Agnostic Meta-Learning(MAML)and ***,we innovatively utilize Proximal Policy Optimization(PPO)to fulfil multi-step local training with a single round of ***,considering the sensitivity of learning rate selection in FRL,we reconstruct the aggregation optimizer with the Federated version of Adam(Fed-Adam)on the server *** experiments demonstrate that,in different environments,FMRL outperforms other FL methods with high training efficiency brought by Fed-Adam.

关键词： federated learning reinforcement learning meta-learning personalization

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Neural dynamics for improving optimiser in deep learning with noise considered

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 722-737页

作者： Dan Su Predrag S.Stanimirović Ling Bo Han Long Jin school of information science and engineering Lanzhou UniversityLanzhouChina Faculty of sciences and Mathematics University of NišNišSerbia school of Electronics and information engineering Guangdong Ocean UniversityZhanjiangChina

As deep learning evolves,neural network structures become increasingly sophisticated,bringing a series of new optimisation *** example,deep neural networks(DNNs)are vulnerable to a variety of *** neural networks under privacy constraints is a method to alleviate privacy leakage,and one way to do this is to add noise to the ***,the existing optimisers suffer from weak convergence in the presence of increased noise during training,which leads to a low robustness of the *** stabilise and improve the convergence of DNNs,the authors propose a neural dynamics(ND)optimiser,which is inspired by the zeroing neural dynamics originated from zeroing neural *** authors first analyse the relationship be-tween DNNs and control ***,the authors construct the ND optimiser to update network ***,the proposed ND optimiser alleviates the non-convergence problem that may be suffered by adding noise to the gradient from different ***,experiments are conducted on different neural network structures,including ResNet18,ResNet34,Inception-v3,MobileNet,and long and short-term memory *** results using CIFAR,YouTube Faces,and R8 datasets demonstrate that the ND optimiser improves the accuracy and stability of DNNs under noise-free and noise-polluted *** source code is publicly available at https://***/LongJin-lab/ND.

关键词： deep learning deep neural networks

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Advancements in the modification of magnesium-based hydrogen storage materials

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Progress in Natural science:Materials International 2024年第3期34卷 540-554页

作者： Lin Yang Wen Zeng Yanqiong Li College of Materials science and engineering Chongqing University school of Electronic information & Electrical engineering Chongqing University of Arts and Sciences

Magnesium-based hydrogen storage materials represent a hydrogen storage technology with broad application prospects. As the global energy crisis and environmental pollution issues become increasingly severe, hydrogen,as a clean and efficient energy source, has garnered growing attention. Magnesium-based hydrogen storage,serving as a crucial means for storing and transporting hydrogen, is gaining prominence due to its abundant resources, low cost, low density, and high hydrogen storage density. However, challenges in terms of absorption/desorption rates, temperature, activation energy, and enthalpy during hydrogen application impede its development. To address these challenges, this paper systematically reviews current research on magnesium-based hydrogen storage materials, encompasses their types, characteristics, and hydrogen absorption ***, it delves into the impacts of nanoscale dimensions, alloying, doping, and catalysis on the performance of magnesium-based materials. The aim is to provide valuable insights for research in related fields.

关键词： For hydrogen storage Magnesium-based materials Research on modifying

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A Feature Selection Method Based on Hybrid Dung Beetle Optimization Algorithm and Slap Swarm Algorithm

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Computers, Materials & Continua 2024年第8期80卷 2979-3000页

作者： Wei Liu Tengteng Ren school of information science and engineering Shenyang Ligong UniversityShenyang110158China

Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification *** recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of *** this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS *** combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)*** evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the *** selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low *** results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization *** addition,the results of statistical tests confirm the significant validity of the method.

关键词： Feature selection dung beetle optimization KNN transfer function HBCSSDBO

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Rethinking the Encoder-decoder Structure in Medical Image Segmentation from Releasing Decoder Structure

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Journal of Bionic engineering 2024年第3期21卷 1511-1521页

作者： Jiajia Ni Wei Mu An Pan Zhengming Chen school of Artificial Intelligence Anhui Polytechnic UniversityWuhu241000China school of information science and engineering HoHai UniversityChangzhou213000China

Medical image segmentation has witnessed rapid advancements with the emergence of encoder-decoder based *** the encoder-decoder structure,the primary goal of the decoding phase is not only to restore feature map resolution,but also to mitigate the loss of feature information incurred during the encoding ***,this approach gives rise to a challenge:multiple up-sampling operations in the decoder segment result in the loss of feature *** address this challenge,we propose a novel network that removes the decoding structure to reduce feature information loss(CBL-Net).In particular,we introduce a Parallel Pooling Module(PPM)to counteract the feature information loss stemming from conventional and pooling operations during the encoding ***,we incorporate a Multiplexed Dilation Convolution(MDC)module to expand the network's receptive ***,although we have removed the decoding stage,we still need to recover the feature map ***,we introduced the Global Feature Recovery(GFR)*** uses attention mechanism for the image feature map resolution recovery,which can effectively reduce the loss of feature *** conduct extensive experimental evaluations on three publicly available medical image segmentation datasets:DRIVE,CHASEDB and MoNuSeg *** results show that our proposed network outperforms state-of-the-art methods in medical image *** addition,it achieves higher efficiency than the current network of coding and decoding structures by eliminating the decoding component.

关键词： Medical image segmentation Encoder-decoder architecture Attention mechanisms Releasing decoder architecture Neural network

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