Predicting tensile-shear strength of nugget using M5P model tree and random forest: An analysis

Dang, Subrat Kumar; Singh, Kulwant

doi:10.1016/j.compind.2020.103345

Cited by 12 publications

(4 citation statements)

References 42 publications

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“…Many trials were investigated and it is found that random forest's efficiency improves as the number of trees grows. It was noted that, after growing 500 trees, RMSE value decreased very slowly and converged after 1000 trees [45]. Thus, the trees number was set to 1000.…”

Section: Findings and Discussionmentioning

confidence: 99%

Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

Taha

Ibrahim

2021

GaEE

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The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

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Section: Findings and Discussionmentioning

confidence: 99%

Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

Taha

Ibrahim

2021

GaEE

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“…As a result, model trees offer significant advantages over regression trees. Dang and Singh (2021) highlight the efficiency and compactness of model trees as their biggest advantage.…”

Section: M5p Model Treementioning

confidence: 99%

Estimation of Tax Loss and Evasion in Turkey with Data Mining Process

ŞENCAN

2023

ETÜSBED

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Vergi kayıp ve kaçakları tüm dünyada olduğu gibi ülkemizde de en büyük sorunlardan biridir. Denetimlerin yanı sıra, istatistiksel teknikler ve makine öğrenimi algoritmaları da vergi kayıp ve kaçaklarının tespitinde büyük önem taşımaktadır. Bu çalışmada vergi kayıp ve kaçak oranı; enflasyon oranı, işsizlik, vergi yükü, cari açık, ekonomik büyüme (GSYİH), devletin büyüklüğü gibi faktörlere bağlı olarak veri madenciliği süreci ile tahmin edilmiştir. Veri madenciliği sürecinde on iki modelleme tekniği kullanılmıştır. Her modelden elde edilen sonuçlar karşılaştırılmış ve bazı istatistiksel göstergeler kullanılarak en iyi model belirlenmiştir. Buna göre, vergi kayıp ve kaçak oranı tahmininde en başarılı sonucu R2, MAE ve RMSE değerleri sırasıyla 0,931, 0,2356 ve 0,2473 olan Gaussian processes modeli vermiştir. Vergi kayıp ve kaçak oranını etkileyen değişkenlerin ağırlık değerleri duyarlılık analizi ile belirlenmiştir. Vergi kayıp ve kaçaklarında pozitif etkisi en yüksek olan faktörlerin işsizlik ve enflasyon oranları olduğu görülmüştür. Bu faktörleri vergi yükü ve GSYİH değerleri izlemektedir. Devletin büyüklüğü ve cari açık faktörlerinin ise vergi kayıp ve kaçak oranı üzerinde negatif etkiye sahip olduğu görülmüştür. Çalışmadan elde edilen sonuçların ülkemizdeki vergi kayıp ve kaçak oranının tahmin edilmesine katkı sağlayacağı düşünülmektedir.

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“…The Random Forest (RF) method is an integrated classifier consisting of multiple decision tree classifiers through automatic aggregation and "bagging" strategies [15]. Compared with traditional decision trees, the RF method solves the problem of complex classification rules that can easily fall into local optimal solutions.…”

Section: Online Evaluation Model For Resistance Spot Welding Qualitymentioning

confidence: 99%

Online evaluation method of resistance spot welding quality based on locally linear embedding algorithm

Zhou,

Pan,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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To address issues of low efficiency, poor feedback timeliness, and unsuitability for fast-paced, high-volume manufacturing of the traditional quality inspection methods of resistance spot welding, an online evaluation method of resistance spot welding quality based on a locally linear embedding algorithm is studied for mild steel resistance spot welding to achieve cost reduction and efficiency improvement. During welding tests, voltage and current were simultaneously collected to calculate the welding power signal. We study the variation pattern of the dynamic power curve. The dynamic power signal was subjected to locally linear embedding and manual feature extraction. The collected features were then used as input to build random forest models and CatBoost models for the online weld quality evaluation, respectively. The results show that the classification models with the feature volumes constructed by locally linear embedding as input have higher assessment accuracy than manually extracted features. The locally linear embedding method can effectively eliminate the subjective influence brought by manual extraction and has better reliability. The CatBoost model based on the locally linear embedding method using the welding power signal can quickly and effectively achieve online quality assessment of mild steel spot welding, providing a further breakthrough in spot welding quality evaluation technology.

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Predicting tensile-shear strength of nugget using M5P model tree and random forest: An analysis

Cited by 12 publications

References 42 publications

Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

Estimation of Tax Loss and Evasion in Turkey with Data Mining Process

Online evaluation method of resistance spot welding quality based on locally linear embedding algorithm

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