Application of SVM Models for Classification of Welded Joints

Marić, Dejan; Duspara, Miroslav; Šolić, Tomislav; Samarđžić, Ivan

doi:10.17559/tv-20180305095253

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Domain-based algorithms, such as item-based hidden semantic models, can implement recommendations on data sets with only positive examples. This is because the basic idea is to draw a slightly larger circle than the positive examples outside the set of positive examples and then recommend those videos or items that are similar to the videos or items they have seen [ 3 , 4 ], but the absence of negative examples means that the learning algorithm is basically impossible to implement due to the fact that learning algorithms mostly draw a surface between positive and negative examples, and if there are no negative examples, there is no surface.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, personalized recommendation technology has been rapidly developed and applied to e-commerce recommendation systems, which can help users to avoid getting lost in the huge amount of item information, help them to make decisions, select the desired items, and increase the sales volume of items [ 3 , 4 ]. In recent years, along with the arrival of “big data era,” information overload, and information explosion, the hidden semantic technology in personalized recommendation technology has gradually become the most mature and successful technology so far [ 5 – 7 ].…”

Section: Introductionmentioning

confidence: 99%

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One Hidden Semantic Model Based on Intergroup Effects for E-Commerce

Zhang

2022

Computational Intelligence and Neuroscience

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E-commerce systems often collect data that clearly express user preferences without considering the remaining negative cases, which gives rise to the hidden semantic problem. In this paper, we improve the original hidden semantic model and propose an intergroup effect model that incorporates users’ historical browsing behavior, user type, and browsing content; by adopting the weighting and add weighting factors, we can predict users’ preferences for different products more accurately and match the candidate products with users’ current behaviors, so as to give more reasonable and effective product recommendation results; by adding the group effect model of user group and product group, we can achieve more accurate prediction of user preferences and make the recommendation more reasonable and effective. The research shows that the hidden semantic method based on intergroup effects information is better than other basic methods at a certain identified evaluation stage. In practice, users’ purchasing preferences change with time, and using a hidden semantic method based on intergroup effects recommendation can effectively improve the recommendation quality of e-commerce recommendation systems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One Hidden Semantic Model Based on Intergroup Effects for E-Commerce

Zhang

2022

Computational Intelligence and Neuroscience

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“…At present, surface defects detection methods mainly include traditional image processing methods and deep learning methods [1][2][3]. Traditional image processing methods detect targets through edge detection, threshold segmentation, feature histogram, classical machine learning methods [2,[4][5][6][7][8][9][10] (support vector machine, k-Nearest Neighbor method and Naive Bayes, neural network, decision tree, etc.). Literature [11] constructed neural network to detect welding defects under alternating/rotating magnetic field, and the test detection accuracy is 94.1%.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Detection Algorithm for Surface Defects of Automobile Door Seals

2022

Teh. vjesn.

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The surface defects of automobile door seals are mainly detected manually at present, which is costly and has low efficiency. Therefore, a deep learning automatic detection algorithm of automobile door seals is studied in this paper. At first, the defects are classified and the data set is made according to the geometric characteristics of the defects. While enhancing the data set, the K-means clustering algorithm is used to cluster the target annotation frame in the data set, and the anchor that matches with the surface defect size of the seal is obtained. Finally, in view of the characteristics of large variation range of defect size, the target detection algorithm YOLOV3 is selected as the basic framework. Meanwhile, considering the high proportion of small and medium-sized targets in defects, the output scale is introduced at 4 times of the sampling position of YOLOV3 backbone network. In order to further enhance the correlation between the extracted features and the channel, spatial position and coordinate position, the feature fusion and attention mechanism module is constructed. The test results show that the mean of the average precision is improved by 4.81% compared with YOLOV3.

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Empirical Analysis of Beneficial Equality of the Basic Medical Insurance for Migrants in China

Gao

Chu

Tao

2021

Discrete Dynamics in Nature and Society

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The fairness of the benefits of basic medical insurance for the migrants is drawing increasing attention. This paper examined the beneficial equality of the basic medical insurance for the floating population in China using the “2014 National Internal Migrant Dynamic Monitoring Survey.” The Heckman model was employed due to selection bias among inpatients, and the random forest algorithm of machine learning was used to analyze the importance of factors affecting the hospitalization decision-making, hospitalization consumption, and reimbursement proportion of the floating population. The results show significant differences in the fairness of basic medical insurance benefits among different income groups, and the highest-income group benefits the most. In contrast, the higher-income group benefits the least. Further verification by introducing the commercial medical insurance indicated that the differences among different income groups did not disappear but reduced the degree of difference among the groups. Although China’s healthcare reform has progressed greatly, the study’s findings confirm that the government’s fair medical insurance can lead to unfair problems and the phenomena of low-income groups subsidizing high-income groups under the equalized basic medical insurance system. Adjusting the design of equalized medical insurance and allowing different income groups to pay different premium levels according to the payment level may be more conducive to the fairness of benefits based on achieving universal health coverage in China.

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Application of SVM Models for Classification of Welded Joints

Cited by 3 publications

References 15 publications

One Hidden Semantic Model Based on Intergroup Effects for E-Commerce

One Hidden Semantic Model Based on Intergroup Effects for E-Commerce

Deep Learning Detection Algorithm for Surface Defects of Automobile Door Seals

Empirical Analysis of Beneficial Equality of the Basic Medical Insurance for Migrants in China

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