International journal of machine learning and computing最新文献_第10页

A Capability Maturity Model for STP aware Software Development 面向STP的软件开发能力成熟度模型

International journal of machine learning and computing Pub Date : 2022-10-05 DOI: 10.53759/7669/jmc202202022

Geim Sllian, Toi Mazur

引用次数: 3

Robustness Analysis of Gaussian Process Convolutional Neural Network with Uncertainty Quantification 不确定性量化高斯过程卷积神经网络的鲁棒性分析

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1097

Mahed Javed, L. Mihaylova, N. Bouaynaya

{"title":"Robustness Analysis of Gaussian Process Convolutional Neural Network with Uncertainty Quantification","authors":"Mahed Javed, L. Mihaylova, N. Bouaynaya","doi":"10.18178/ijmlc.2022.12.5.1097","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1097","url":null,"abstract":" Abstract —This paper presents a novel framework for image classification which comprises a convolutional neural network (CNN) feature map extractor combined with a Gaussian process (GP) classifier. Learning within the CNN-GP involves forward propagating the predicted class labels, then followed by backpropagation of the maximum likelihood function of the GP with a regularization term added. The regularization term takes the form of one of the three loss functions: the Kullback-Leibler divergence, Wasserstein distance, and maximum correntropy. The training and testing are performed in mini batches of images. The forward step (before the regularization) involves replacing the original images in the mini batch with their close neighboring images and then providing these to the CNN-GP to get the new predictive labels. The network performance is evaluated on MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 datasets. Precision-recall and receiver operating characteristics curves are used to evaluate the performance of the GP classifier. The proposed CNN-GP performance is validated with different levels of noise, motion blur, and adversarial attacks. Results are explained using uncertainty analysis and further tests on quantifying the impact on uncertainty with attack strength are carried out. The results show that the testing accuracy improves for networks that backpropagate the maximum likelihood with regularized losses when compared with methods that do not. Moreover, a comparison with a state-of-art CNN Monte Carlo dropout method is presented. The outperformance of the CNN-GP framework with respect to reliability and computational efficiency is","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42243897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rotated Grid Search for Hyperparameter Optimization 旋转网格搜索超参数优化

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1110

引用次数: 0

Mining Weighted Erasable Itemsets Over the Incremental Database Based on the InvP-List 基于InvP-List的增量数据库加权可擦除项集挖掘

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1106

Ye, In Chang, Siang, Jia Du, Chin, Ting Lin

{"title":"Mining Weighted Erasable Itemsets Over the Incremental Database Based on the InvP-List","authors":"Ye, In Chang, Siang, Jia Du, Chin, Ting Lin","doi":"10.18178/ijmlc.2022.12.5.1106","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1106","url":null,"abstract":"An erasable itemset is the low profit itemset in the product database. The previous algorithms for mining erasable itemsets ignore the weight of each component of the product and mine erasable itemsets by concerning the product profit only in static product databases. But, when we consider the weight of each component, previous algorithms for mining weighted erasable itemsets would violate the anti-monotone property. That is, the subset X of an erasable pattern Y may not be an erasable pattern. The IWEI algorithm uses the static overestimated factor of itemsets profits to satisfy the “anti-monotone property” of weighted erasable itemset and constructs the IWEI-Tree and OP-List data structure for the dynamic database. However, the IWEI-Tree has to be reconstructed, when reading the whole product database is finished. It will take long time to complete the mining of the whole tree, if the database is frequently updated. The IWEI algorithm generates the too low static value of the overestimated factor to prune candidates. To solve those problems, in this paper, we propose the Inverted-Product-List algorithm (InvP-List) and with the local estimated factor to identify weighted erasable itemsets candidates from the Candidate-List which is generated from InvP-List. We propose the appropriate estimated factor to reduce the number of candidates which is called LMAW. LMAW is a local estimated factor which is used to check whether the itemset is a weighted erasable itemset or not. Our InvP-List algorithm also requires only one database scan. Moreover, our proposed algorithm concerning the local estimated factor creates few numbers of candidates than the IWEI algorithm. From the performance study, we show that our InvP-List algorithm is more efficient than the IWEI algorithm both in the real and the synthetic datasets.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47439723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Straightness Prediction in CNC Turning Process for Carbon Steel and Aluminum Workpieces Applying Artificial Neural Networks 应用人工神经网络预测碳钢和铝工件数控车削过程中的直线度

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1098

S. Tangjitsitcharoen, W. Laiwatthanapaisan

{"title":"Straightness Prediction in CNC Turning Process for Carbon Steel and Aluminum Workpieces Applying Artificial Neural Networks","authors":"S. Tangjitsitcharoen, W. Laiwatthanapaisan","doi":"10.18178/ijmlc.2022.12.5.1098","DOIUrl":"https://doi.org/10.18178/ijmlc.2022.12.5.1098","url":null,"abstract":" Abstract —An intelligent machine and manufacturing system has a significant role in the near future, especially when the circumstance of manufacturing industries are seriously competitive. New technologies are continuously being developed to serve future manufacturing. CNC turning machine is widely utilized in various advanced manufacturing industries. Straightness is a critical parameter in CNC turning process, which affects the workpiece assembly directly. However, control of straightness of the workpieces during in-process turning is difficult to be measured. Moreover, CNC turning machine cannot be adjusted real-time without stopping the operation. Hence, the aim of this research is to develop the straightness prediction model in the CNC turning process under various cutting conditions for carbon steel and aluminum workpieces in order to improve in-process monitoring and control of straightness. The cutting forces ratio has been adopted to estimate straightness. The Daubechies wavelet transform is utilized to decompose the dynamic cutting forces to remove the noise signals for better prediction. The straightness is calculated by employing the two-layer feed forward neural network, which is trained with the Levenberg-Marquardt back-propagation algorithm. As a result, the in-process straightness could be predicted well with greater accuracy and reliability using the proposed straightness","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47397241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structure Level Pruning of Efficient Convolutional Neural Networks with Sparse Group LASSO 稀疏群LASSO有效卷积神经网络的结构级修剪

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1111

引用次数: 0

A Machine Learning Approach for the Classification of Lower Back Pain in the Human Body 一种用于人体下背痛分类的机器学习方法

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1105

引用次数: 0

A New Approach to Neural Network Design for Fast Convergence via Feed-forward Loop 一种基于前馈环路的神经网络快速收敛设计新方法

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1109

引用次数: 0

Lifespan Prediction for Lung and Bronchus Cancer Patients via Machine Learning Techniques 利用机器学习技术预测癌症肺和支气管患者的寿命

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1107

引用次数: 0

Early Grade Prediction Using Profile Data 利用剖面数据进行早期品位预测

International journal of machine learning and computing Pub Date : 2022-09-01 DOI: 10.18178/ijmlc.2022.12.5.1100

S. Iqbal, Jerin Ishrat Natasha

引用次数: 0