Comparative study of the VINCI Robot's arm end effector matrix using Python and MATLAB

Tamanna,; Uddin, Moin; Kumar, Virendra; Yadav, Vinod Kumar

doi:10.1016/j.matpr.2021.02.429

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…The performances of MATLAB and Python compared to each other has been a subject of debate over the years. In this context, the performance of MATLAB has been comparatively assessed with Python for different applications in various fields [41][42][43][44][45][46]. In [41], the aim was to offer a well-structured teaching language that enabled engineering students to quickly express their algorithms.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

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Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Yilmaz

Akdag²,

Dalveren

et al. 2023

Diagnostics

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Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

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Section: Related Work and Research Gapsmentioning

confidence: 99%

“…In [45], the runtimes of the programming environments were compared for complex number calculations used in electromagnetic applications. In [46], accuracies of the programming environments for the computation of a robotic arm end effector matrix were compared.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Yilmaz

Akdag²,

Dalveren

et al. 2023

Diagnostics

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“…Python's variety of libraries makes it superior for developing advanced machine learning models. In addition, most of the machine learning projects currently developed are done with Python, which makes transfer learning possible and seamless [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

Amenaghawon,

Igemhokhai,

Eshiemogie

et al. 2024

Heliyon

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Comparative study of the VINCI Robot's arm end effector matrix using Python and MATLAB

Cited by 2 publications

References 7 publications

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

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