Imed Kacem, Ahmed Kadri, Pierre Laroche

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 421-430, 2018, DOI:10.31209/2018.100000016

Abstract This paper addresses an inventory regulation problem in bicycle sharingsystems. The problem is to balance a network consisting of a set of stations by using a single vehicle, with the aim of minimizing the weighted sum of the waiting times during which some stations remain imbalanced. Motivated by the complexity of this problem, we propose a two-stage procedure based on decomposition. First, the network is divided into multiple zones by using two different clustering strategies. Then, the balancing problem is solved in each zone. Finally, the order in which the zones must be visited is More >

Li-Hong Juang^a, Ming-Ni Wu^b

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 257-266, 2018, DOI:10.1080/10798587.2016.1272778

Abstract In this paper we explore these math approaches for medical image applications. The application of the proposed method for detection tumor will be able to distinguish exactly tumor size and region. In this research, some major design and experimental results of tumor objects detection method for medical brain images is developed to utilize an automatic multi-thresholding method to handle this problem by combining the histogram analysis and the Otsu clustering. The histogram evaluations can decide the superior number of clusters firstly. The Otsu classification algorithm solves the given medical image by continuously separating the input 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 >

Kehua Yang^1,2,*, Tian Tan¹, Wei Zhang¹

CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 269-281, 2018, DOI:10.32604/cmc.2018.03696

Abstract Dempster-Shafer (D-S) evidence theory is a key technology for integrating uncertain information from multiple sources. However, the combination rules can be paradoxical when the evidence seriously conflict with each other. In the paper, we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) density clustering. In the proposed mechanism, firstly, the original evidence sets are preprocessed by DBSCAN density clustering, and a successfully focal element similarity criteria is used to mine the potential information between the evidence, and make a correct measure of the conflict evidence. Then, two More >

Lianggui Liu^1,*, Wei Li¹, Huiling Jia¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511

Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the More >

Xuewen Zhang¹, Zhonghao Li¹, Gongshen Liu^1,*, Jiajun Xu¹, Tiankai Xie², Jan Pan Nees¹

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 405-417, 2018, DOI:10.3970/cmc.2018.02527

Abstract As a main distributed computing system, Spark has been used to solve problems with more and more complex tasks. However, the native scheduling strategy of Spark assumes it works on a homogenized cluster, which is not so effective when it comes to heterogeneous cluster. The aim of this study is looking for a more effective strategy to schedule tasks and adding it to the source code of Spark. After investigating Spark scheduling principles and mechanisms, we developed a stratifying algorithm and a node scheduling algorithm is proposed in this paper to optimize the native scheduling 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 >

Sunil Kr. Jha¹, Zulfiqar Ahmad²

CMES-Computer Modeling in Engineering & Sciences, Vol.113, No.4, pp. 443-459, 2017, DOI:10.3970/cmes.2017.113.443

Abstract Microbial population and enzyme activities are the significant indicators of soil strength. Soil microbial dynamics characterize microbial population and enzyme activities. The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics, like rock phosphate solubilization, bacterial population, and ACC-deaminase activity. More specifically, optimized subtractive clustering (SC) and Wang and Mendel's (WM) fuzzy inference systems (FIS) have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics. Experimental measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as More >

R.D. Phillips¹, M.S. Hossain¹, L.T. Watson^1,2, R.H. Wynne³, Naren Ramakrishnan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.97, No.2, pp. 175-197, 2014, DOI:10.3970/cmes.2014.097.175

Abstract Clusters, typically mined by modeling locality of attribute spaces, are often evaluated for their ability to demonstrate ‘enrichment’ of categorical features. A cluster enrichment procedure evaluates the membership of a cluster for significant representation in predefined categories of interest. While classical enrichment procedures assume a hard clustering definition, this paper introduces a new statistical test that computes enrichments for soft clusters. Application of the new test to several scientific datasets is given. More >

Kai Wang¹, Guojian Li¹, Qiang Wang^1,2, Huimin Wang¹, Jiaojiao Du¹, Jicheng He¹

CMC-Computers, Materials & Continua, Vol.38, No.2, pp. 79-89, 2013, DOI:10.3970/cmc.2013.038.079

Abstract Study on the behaviors of the melting and coalescence of clusters in atomic scale may create new structure at nanoscale, which is a very important research field. The structural evolutions of clusters Cu₃₂₁, Co₃₂₁, and Ni₃₂₁ during their melting and coalescence processes were studied using molecular dynamics simulation with a general embedded atom method in this paper. It was found that the geometries of Cu₃₂₁ and Co₃₂₁ transformed to icosahedron from fcc near their melting points, which leads to the increase of their melting points. Concerning the coalescence, it was found that Cu atoms easily formed a coating More >