7 results
Search Results
Now showing 1 - 7 of 7
Conference Object Citation - WoS: 3A Software Metric for Python Language(Springer-verlag Berlin, 2010) Misra, Sanjay; Cafer, FeridThere are many metrics for evaluating the quality of codes written in different programming languages. However, no efforts have been done to propose metrics for Python, which is an important and useful language especially for the software development for the embedded systems. In this present work, we are trying to investigate all the factors, which are responsible for increasing the complexity of code written in Python language. Accordingly, we have proposed a unified metric for this language. Practical applicability of the metric is demonstrated on a case study.Conference Object A Multi-Paradigm Complexity Metric (mcm)(2011) Misra,S.; Akman,I.; Cafer,F.Huge amount of researches and software metrics have been proposed for procedural and object-oriented languages. However, there are only few metrics available in the literature related with multi-paradigm programming languages. In this paper, we propose a metric to evaluate the code written in multi-paradigm language. Our proposed metric can be used for most of the programming paradigms, including both procedural and object-oriented languages. © 2011 Springer-Verlag.Conference Object Citation - WoS: 2A Multi-Paradigm Complexity Metric (mcm)(Springer-verlag Berlin, 2011) Misra, Sanjay; Akman, Ibrahim; Cafer, FeridHuge amount of researches and software metrics have been proposed for procedural and object-oriented languages. However, there are only few metrics available in the literature related with multi-paradigm programming languages. In this paper, we propose a metric to evaluate the code written in multi-paradigm language. Our proposed metric can be used for most of the programming paradigms, including both procedural and object-oriented languages.Conference Object Citation - Scopus: 1A Software Metric for Python Language(Springer Verlag, 2010) Misra,S.; Cafer,F.There are many metrics for evaluating the quality of codes written in different programming languages. However, no efforts have been done to propose metrics for Python, which is an important and useful language especially for the software development for the embedded systems. In this present work, we are trying to investigate all the factors, which are responsible for increasing the complexity of code written in Python language. Accordingly, we have proposed a unified metric for this language. Practical applicability of the metric is demonstrated on a case study. © 2010 Springer-Verlag Berlin Heidelberg.Conference Object Citation - Scopus: 3A Multi-Paradigm Complexity Metric (mcm)(2011) Misra,S.; Akman,I.; Cafer,F.Huge amount of researches and software metrics have been proposed for procedural and object-oriented languages. However, there are only few metrics available in the literature related with multi-paradigm programming languages. In this paper, we propose a metric to evaluate the code written in multi-paradigm language. Our proposed metric can be used for most of the programming paradigms, including both procedural and object-oriented languages. © 2011 Springer-Verlag.Article Citation - WoS: 3Citation - Scopus: 6Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection From Mri Images(Mdpi, 2023) Yilmaz, Vadi Su; Akdag, Metehan; Dalveren, Yaser; Doruk, Resat Ozgur; Kara, Ali; Soylu, AhmetBrain 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.Article Citation - WoS: 7Estimating Complexity of Programs in Python Language(Univ Osijek, Tech Fac, 2011) Misra, Sanjay; Cafer, FeridIn this paper, a complexity metric for Python language is formulated. Since Python is an object oriented language, the present metric is capable to evaluate any object-oriented language. We validate our metric with case study, comparative study and empirical validation. The case study is in Python, Java and C++ and the results prove that Python is better than other object-oriented languages. Later, we validate the metric empirically with a real project, which is developed in Python.
