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 >

Geng Guo^1,*, Leiwen Chen¹, Ji Li², Shu Yan³, Wenxiang Wu⁴, Lingxu Li⁵, Hongda Li⁶

Energy Engineering, Vol.117, No.2, pp. 79-88, 2020, DOI:10.32604/EE.2020.010529 - 23 April 2020

Abstract Featured by high energy density, low combustion pollution and large quantity, natural gas hydrate has become one of the research hotspots in Sanlutian Field of Muri Coalfield since 2008, when China first drilled natural gas hydrate samples in the permafrost area of Qilian Mountains, Qinghai-Tibet Plateau. However, the study on the controlling factors of gas hydrate accumulation is still shallow, which hinders the exploration and development of natural gas hydrate resources. The controlling factors of gas hydrate accumulation mainly include temperature and pressure conditions, gas source conditions, sedimentary conditions and structural conditions, among which structural More >

Zhongxu Yin^{1, *}, Yiran Song², Huiqin Chen³, Yan Cao⁴

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1013-1029, 2020, DOI:10.32604/cmc.2020.09345 - 01 May 2020

Abstract Security-sensitive functions are the basis for building a taint-style vulnerability model. Current approaches for extracting security-sensitive functions either don’t analyze data flow accurately, or not conducting pattern analyzing of conditions, resulting in higher false positive rate or false negative rate, which increased manual confirmation workload. In this paper, we propose a security sensitive function mining approach based on preconditon pattern analyzing. Firstly, we propose an enhanced system dependency graph analysis algorithm for precisely extracting the conditional statements which check the function parameters and conducting statistical analysis of the conditional statements for selecting candidate security sensitive More >

Abeer I. Alhujaylan^{1, 2, *}, Manar I. Hosny¹

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1025-1051, 2020, DOI:10.32604/cmc.2020.08742

Abstract Mobile commerce (m-commerce) contributes to increasing the popularity of electronic commerce (e-commerce), allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time. As demand for e-commerce increases tremendously, the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer satisfaction. One important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem (MVPPDP), where a selected set of pickup and delivery customers need to be served within certain allowed trip time. In this paper, we proposed hybrid clustering algorithms More >

Yanbin Zhao^{1, *}, Bin Du², Shuang Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 235-257, 2020, DOI:10.32604/cmes.2020.08070 - 01 January 2020

Abstract Satellite observation schedule is investigated in this paper. A mission planning algorithm of task clustering is proposed to improve the observation efficiency of agile satellite. The newly developed method can make the satellite observe more targets and therefore save observation resources. First, for the densely distributed target points, a pre-processing scheme based on task clustering is proposed. The target points are clustered according to the distance condition. Second, the local observation path is generated by Tabu algorithm in the inner layer of cluster regions. Third, considering the scatter and cluster sets, the global observation path More >

Zhuo Zhou¹, Jiaohua Qin^1,*, Xuyu Xiang¹, Yun Tan¹, Qiang Liu¹, Neal N. Xiong²

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06431

Abstract Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data, this paper takes news text as the research object and proposes LDA text topic clustering algorithm based on Spark big data platform. Since the TF-IDF (term frequency-inverse document frequency) algorithm under Spark is irreversible to word mapping, the mapped words indexes cannot be traced back to the original words. In this paper, an optimized method is proposed that TF-IDF under Spark to ensure the text words can be restored. Firstly, the text feature is extracted by More >

Ahmed A. M. Jamel^1,∗, Bahriye Akay^2,†

Computer Systems Science and Engineering, Vol.34, No.5, pp. 259-281, 2019, DOI:10.32604/csse.2019.34.259

Abstract Parallel processing has turned into one of the emerging fields of machine learning due to providing consistent work by performing several tasks simultaneously, enhancing reliability (the presence of more than one device ensures the workflow even if some devices disrupted), saving processing time and introducing low cost and high-performance computation units. This research study presents a survey of parallel K-means and Fuzzy-c-means clustering algorithms based on their implementations in parallel environments such as Hadoop, MapReduce, Graphical Processing Units, and multi-core systems. Additionally, the enhancement in parallel clustering algorithms is investigated as hybrid approaches in which More >

Wangdong Jiang¹, Taian Yang^{1, *}, Guang Sun^{1, 3}, Yucai Li¹, Yixuan Tang², Hongzhang Lv¹, Wenqian Xiang¹

Journal on Big Data, Vol.1, No.3, pp. 117-134, 2019, DOI:10.32604/jbd.2019.08454

Abstract In order to study deeply the prominent problems faced by China’s clean government work, and put forward effective coping strategies, this article analyzes the network information of anti-corruption related news events, which is based on big data technology. In this study, we take the news report from the website of the Communist Party of China (CPC) Central Commission for Discipline Inspection (CCDI) as the source of data. Firstly, the obtained text data is converted to word segmentation and stop words under preprocessing, and then the pre-processed data is improved by vectorization and text clustering, finally,… More >

Lubna Alhenak¹, Manar Hosny^1,*

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1045-1074, 2019, DOI:10.32604/cmc.2019.08355

Abstract In recent years, the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web. As a result, the use of techniques for extracting useful information from large collections of data, and particularly documents, has become more necessary and challenging. Text clustering is such a technique; it consists in dividing a set of text documents into clusters (groups), so that documents within the same cluster are closely related, whereas documents in different clusters are as different as possible. Clustering depends on measuring the content (i.e., words) of… More >