S. Eliza Femi Sherley^1,*, J. M. Karthikeyan¹, N. Bharath Raj¹, R. Prabakaran², A. Abinaya¹, S. V. V. Lakshmi³

Journal on Artificial Intelligence, Vol.4, No.4, pp. 215-227, 2022, DOI:10.32604/jai.2022.034931

Abstract Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens. To overcome this limitation, a… More >

Israr Ghani^*, Wan M. N. Wan-Kadir, Adila Firdaus Arbain, Noraini Ibrahim

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 635-650, 2022, DOI:10.32604/cmc.2022.025027

Abstract Regression testing (RT) is an essential but an expensive activity in software development. RT confirms that new faults/errors will not have occurred in the modified program. RT efficiency can be improved through an effective technique of selected only modified test cases that appropriate to the modifications within the given time frame. Earlier, several test case selection approaches have been introduced, but either these techniques were not sufficient according to the requirements of software tester experts or they are ineffective and cannot be used for available test suite specifications and architecture. To address these limitations, we recommend an improved and efficient… More >

Anju Asokan¹, J. Anitha¹, Bogdan Patrut², Dana Danciulescu³, D. Jude Hemanth^1,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 373-388, 2021, DOI:10.32604/cmc.2020.012364

Abstract Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features. Then… More >