Sudan Jha¹, Gyanendra Prasad Joshi², Lewis Nkenyereya³, Dae Wan Kim^{4, *}, Florentin Smarandache⁵

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1203-1220, 2020, DOI:10.32604/cmc.2020.011618 - 20 August 2020

Abstract Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University More >

Sujit Sil^{1, 2}, Tanmoy Mallick², Tuhin Pal¹, Animesh Mondal¹, Kalyan Kumar De¹ and Asok Ghosh^2,*

Phyton-International Journal of Experimental Botany, Vol.88, No.3, pp. 295-315, 2019, DOI:10.32604/phyton.2019.06907

Abstract The genus Saraca L. (Leguminosae) is a universal panacea in herbal medicine. The present study investigates the comparative pollen morphology of four species of Saraca viz. S. asoca (Roxb.) de Wilde, S. declinata (Jack) Miq., S. indica L., and S. thaipingensis Cantley ex Prain growing in India to reveal differences of their pollen structures to aid taxonomic and evolutionary values. The detailed morphology and surface structure of pollen grains were studied and described using light microscopy and scanning electron microscopy. The pollen grains of Saraca showed isopolar, para-syncolporate, tricolporate, with radially symmetric, prolate and prolate-spheroidal… More >

Lili He^1,2, Dantong Ouyang^1,2, Meng Wang^1,2, Hongtao Bai^1,2, Qianlong Yang^1,2, Yaqing Liu^3,4, Yu Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 549-570, 2018, DOI:10.32604/cmc.2018.03840

Abstract In this paper, the clustering analysis is applied to the satellite image segmentation, and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power. The method firstly adopts the fuzzy C-means clustering (FCM) to obtain the satellite cloud image segmentation. Secondly, in the cloud image, we dispose the ‘high-density connected’ pixels in the same cloud clusters and the ‘low-density connected’ pixels in different cloud clusters. Therefore, we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge. Finally, using the method More >

Abozeid A^1,2, Y Liu¹, J Liu¹, ZH Tang¹

Phyton-International Journal of Experimental Botany, Vol.86, pp. 306-317, 2017, DOI:10.32604/phyton.2017.86.306

Abstract The genus Vicia L. belongs to the tribe Vicieae of the Fabaceae family. The genus includes about 190 species, from which about 40 species have economic importance. Some of them are food crops, but more than a dozen are forage plants. In this study, leaves of Vicia species from China, USA and Argentina were examined using stereo-microscopy and light microscopy. We determined macro- and micro-morphological characteristics that could be of taxonomic use. Forty eight characteristics of each taxon were determined including petiole and tendril length; leaflets number, length, width, shape, apex, base; blade surface, trichome shape, More >

S. Yotte¹, J. Riss, D. Breysse, S. Ghosh

CMES-Computer Modeling in Engineering & Sciences, Vol.5, No.2, pp. 171-188, 2004, DOI:10.3970/cmes.2004.005.171

Abstract Particle distribution influences the particulate reinforced metal matrix composites (PMMC). The knowledge of particle distribution is essential for material design. The study of particle distribution relies on analysis of material images. In this paper three methods are used on an image of an Al/SiC composite. The first method consists in applying successive dilations to the image. At each step the number of objects and the total object area are determined. The decrease of the number of objects as a function of the area is an indicator of characteristic distances. The second method is based on… More >