In this paper an attempt has been made to identify the important determinants of retained earnings in profitable steel companies in steel sector of India and which have impact on the retention of earnings of steel companies under study. Multiple linear regression is used to identify the determinants of retained earnings for a period of sixteen years. Also the importance of retained earnings as a source of finance for steel sector companies is also studied in the paper.
Model‐based test case generation techniques provide a mechanism to derive tests systematically. This study provides a systematic mapping of test case generation techniques based on UML interaction diagrams. The study compares the test case generation techniques regarding their capabilities and limitations, and it also assesses the reporting quality of the primary studies. We can conclude that the studies presenting test case generation techniques using UML interaction diagrams were not following the guidelines for research methods (eg, case studies or experiments). Solutions were not empirically evaluated in industrial contexts. Our study revealed that better tool support is needed to introduce the UML interaction diagram–based test case generation techniques in the industry.
A brain tumor is the most common and destructive disease which takes the patient to the end of life. Thus, treatment planning is a key stage to enhance the quality of patient life. There are various image techniques such as computed tomography, Magnetic Resonance Imaging (MRI), and ultrasound imaging used to measure the Tumor in the brain, lungs, liver, and so on. Classification of Tumor and non-tumor can be done, but it has some limitations, like only a limited number of images can be measured accurately and quantitatively. Therefore, an automatic classification scheme plays an important role in preventing the death rate of human beings, rendering us a challenging task. For this purpose, we take some MRI images to classify Tumor by examining the huge amount of data generated by MRI scan. In this work, brain tumor classification is proposed by extended Convolutional Neural Network (CNN); a deeper convolution layer is designed to improve the performance by using a small kernel size. Surgeons and radiologists can classify brain tumors more easily and effectively using this technique. Extended CNN achieves approximately 99% accuracy rate by showing the experimental results onward.
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