R.V.V. Krishna^1,*, S. Srinivas Kumar²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 281-290, 2020, DOI:10.31209/2019.100000121

Abstract In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the one -against-all multi class support vector machine (MSVM) classifier for segmentation. Local Binary Pattern is used for extracting the textural features and L*a*b color model is used for obtaining the color features. The MSVM is trained using the samples obtained from a novel soft rough fuzzy c-means (SRFCM) clustering. The fuzzy set based membership functions capably handle the problem of overlapping clusters. The lower and upper approximation concepts of rough sets deal well More >

Gongde Guo¹, Kai Yu¹, Hui Wang², Song Lin^{1, *}, Yongzhen Xu¹, Xiaofeng Chen³

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1397-1409, 2020, DOI:10.32604/cmc.2020.011399 - 20 August 2020

Abstract As an important branch of machine learning, clustering analysis is widely used in some fields, e.g., image pattern recognition, social network analysis, information security, and so on. In this paper, we consider the designing of clustering algorithm in quantum scenario, and propose a quantum hierarchical agglomerative clustering algorithm, which is based on one dimension discrete quantum walk with single-point phase defects. In the proposed algorithm, two nonclassical characters of this kind of quantum walk, localization and ballistic effects, are exploited. At first, each data point is viewed as a particle and performed this kind of… More >

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 >

Ibrahim Arpaci¹, Shadi Alshehabi², Mostafa Al-Emran^{3, *}, Mahmoud Khasawneh⁴, Ibrahim Mahariq⁴, Thabet Abdeljawad^{5, 6, 7}, Aboul Ella Hassanien^{8, 9}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 193-204, 2020, DOI:10.32604/cmc.2020.011489 - 23 July 2020

Abstract People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention… More >

Xinliang Tang¹, Man Zhang¹, Pingping Yu¹, Wei Liu², Ning Cao^{3, *}, Yunfeng Xu⁴

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1725-1739, 2020, DOI:10.32604/cmc.2020.010272 - 30 June 2020

Abstract In a large-scale wireless sensor network (WSN), densely distributed sensor nodes process a large amount of data. The aggregation of data in a network can consume a great amount of energy. To balance and reduce the energy consumption of nodes in a WSN and extend the network life, this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm. The algorithm uses a clustering method to form and optimize clusters, and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN. To ensure that More >

Bolun Chen^{1, 2}, Guochang Zhu^{1, *}, Min Ji¹, Yongtao Yu¹, Jianyang Zhao¹, Wei Liu³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1039-1049, 2020, DOI:10.32604/cmc.2020.010583 - 10 June 2020

Abstract Air quality prediction is an important part of environmental governance. The accuracy of the air quality prediction also affects the planning of people’s outdoor activities. How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention, pollution control and pollution early warning. In this paper, we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants. Firstly, the data of air pollutants in different cities… More >

V. Manikandan^{1, *}, M. Sivaram¹, Amin Salih Mohammed², V. Porkodi³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 753-776, 2020, DOI:10.32604/cmc.2020.010267 - 10 June 2020

Abstract Wireless Sensor Networks (WSNs) comprises low power devices that are randomly distributed in a geographically isolated region. The energy consumption of nodes is an essential factor to be considered. Therefore, an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime. This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication. This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies. Time Division Multiple Access (TDMA) scheduler is also improved… More >

Lili He^{1, 2}, Zhiwei Cai^{1, 2}, Dantong Ouyang^{1, 2}, Changshuai Wang^{1, 2}, Yu Jiang^{1, 2}, Chong Wang^{1, 2, 3}, Hongtao Bai^{1, 2, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 373-388, 2020, DOI:10.32604/cmc.2020.09860 - 20 May 2020

Abstract In view of the satellite cloud-derived wind inversion has the characteristics of large scale, intensive computing and time-consuming serial inversion algorithm is very difficult to break through the bottleneck of efficiency. We proposed a parallel acceleration scheme of cloud-derived wind inversion algorithm based on MPI cluster parallel technique in this paper. The divide-and-conquer idea, assigning winds vector inversion tasks to each computing unit, is identified according to a certain strategy. Each computing unit executes the assigned tasks in parallel, namely divide-and-rule the inversion task, so as to reduce the efficiency bottleneck of long inversion time… More >

Yu Jiang^{1, 2}, Dengwen Yu¹, Mingzhao Zhao^{1, 2}, Hongtao Bai^{1, 2}, Chong Wang^{1, 2, 3}, Lili He^{1, 2, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 207-216, 2020, DOI:10.32604/cmc.2020.09861 - 20 May 2020

Abstract Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning. This paper implements and compares unsupervised and semi-supervised clustering analysis of BOAArgo ocean text data. Unsupervised K-Means and Affinity Propagation (AP) are two classical clustering algorithms. The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range. Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition, and use this data for semi-supervised… More >

Nithya Rekha Sivakumar^{1, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 81-96, 2020, DOI:10.32604/cmc.2020.08851 - 20 May 2020

Abstract In the past few decades, Energy Efficiency (EE) has been a significant challenge in Wireless Sensor Networks (WSNs). WSN requires reduced transmission delay and higher throughput with high quality services, it further pays much attention in increased energy consumption to improve the network lifetime. To collect and transmit data Clustering based routing algorithm is considered as an effective way. Cluster Head (CH) acts as an essential role in network connectivity and perform data transmission and data aggregation, where the energy consumption is superior to non-CH nodes. Conventional clustering approaches attempts to cluster nodes of same… More >