K. Naveen Kumar¹, K. Srinivasa Rao², Y. Srinivas³, Ch. Satyanarayana⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.107, No.3, pp. 201-221, 2015, DOI:10.3970/cmes.2015.107.201

Abstract Texture Analysis is one of the prime considerations for image analysis and processing. Texture segmentation gained lot of importance due to its ready applicability in automation of scene identification and computer vision. Several texture segmentation methods have been developed and analysed with the assumption that the feature vector associated with the texture of the image region is modelled as Gaussian mixture model. Due to the limitations of the Gaussian model being meso kurtic, it may not characterise the texture of all image regions accurately. Hence in this paper, a texture segmentation algorithm is developed and analysed with the assumption that… More >

Yan Liu^1,*, Lian Liu¹, Yu Yan², Hao Feng¹, Shichang Ding³

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1123-1139, 2019, DOI:10.32604/cmc.2019.05619

Abstract Social organizations can be represented by social network because it can mathematically quantify and represent complex interrelated organizational behavior. Exploring the change in dynamic social network is essential for the situation awareness of the corresponding social organization. Social network usually evolves gradually and slightly, which is hard to be noticed. The statistical process control techniques in industry field have been used to distinguish the statistically significant change of social network. But the original method is narrowed due to some limitation on measures. This paper presents a generic framework to address the change detection problem in dynamic social network and introduces… More >

P. Yuvaraj¹, A. Ramachandra Murthy², Nagesh R. Iyer³, Pijush Samui⁴, S.K. Sekar⁵

CMC-Computers, Materials & Continua, Vol.36, No.1, pp. 73-97, 2013, DOI:10.3970/cmc.2013.036.073

Abstract This paper presents Multivariate Adaptive Regression Splines (MARS) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (GF), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). This paper also presents the details of development of MARS model to predict failure load (Pmax) of high strength concrete (HSC) and ultra high strength concrete (UHSC) beam specimens. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack… More >