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Zhi Quan, Hailong Zhang, Jiyu Luo and Haijun Sun
Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR)...
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Shiyuan Zhu, Yuwei Zhao and Shihong Yue
Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data obje...
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Yuanyou Ou and Baoning Niu
The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problem...
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Yiming Tang, Rui Chen and Bowen Xia
Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VS...
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Tran Dinh Khang, Manh-Kien Tran and Michael Fowler
Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering...
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Tran Dinh Khang, Nguyen Duc Vuong, Manh-Kien Tran and Michael Fowler
Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering tech...
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Shengping Lv, Rongheng Xian, Denghui Li, Binbin Zheng and Hong Jin
The application of the work is to optimize the material feeding of a printed circuit board (PCB) template and therefore reduce the comprehensive cost caused by surplus and supplemental feeding.
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Yanyan Li, Honggang Wang, Jianping Chen and Yanjun Shang
Debris flows in the Wudongde dam area, China could pose a huge threat to the running of the power station. Therefore, it is of great significance to carry out a susceptibility analysis for this area. This paper presents an application of the rock enginee...
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Yanyan Li, Honggang Wang, Jianping Chen, Yanjun Shang
Pág. 1 - 15
Debris flows in the Wudongde dam area, China could pose a huge threat to the running of the power station. Therefore, it is of great significance to carry out a susceptibility analysis for this area. This paper presents an application of the rock enginee...
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Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane and Hassan Ouajji
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Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane and Hassan Ouajji
The aim of this paper is to present a mobile agents model for distributed classification of Big Data. The great challenge is to optimize the communication costs between the processing elements (PEs) in the parallel and distributed computational models by...
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Ali Mulyanto,Romi Satria Wahono
Pág. 43 - 48
Klasterisasi fuzzy merupakan masalah penting yang merupakan subjek penelitian aktif dalam beberapa aplikasi dunia nyata. Algoritma fuzzy c-means (FCM) merupakan salah satu teknik pengelompokan fuzzy yang paling populer karena efisien, dan mudah dii...
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Xiao Liu, Xu Lai and Jin Zou
In this paper, a missing wind speed data temporal interpolation and extrapolation method in the wind energy industry was investigated. Given that traditional methods have previously ignored part of mixed uncertainty of wind speed, a concrete granular com...
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Sheng-Chieh Chang, Wei-Ching Chuang and Jin-Tsong Jeng
Symbolic interval data analysis (SIDA) has been successfully applied in a wide range of fields, including finance, engineering, and environmental science, making it a valuable tool for many researchers for the incorporation of uncertainty and imprecision...
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Zeynel Cebeci and Cagatay Cebeci
The goal of partitioning clustering analysis is to divide a dataset into a predetermined number of homogeneous clusters. The quality of final clusters from a prototype-based partitioning algorithm is highly affected by the initially chosen centroids. In ...
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Md Shahariar Alam, Md Mahbubur Rahman, Mohammad Amazad Hossain, Md Khairul Islam, Kazi Mowdud Ahmed, Khandaker Takdir Ahmed, Bikash Chandra Singh and Md Sipon Miah
In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human b...
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In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human b...
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Fengmei Liang, Yajun Xu, Weixin Li, Xiaoling Ning, Xueou Liu and Ajian Liu
To overcome the limitation of artificial judgment of meibomian gland morphology, we proposed a solution based on an improved fuzzy c-means (FCM) algorithm and rough sets theory. The rough sets reduced the redundant attributes while ensuring classificatio...
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Xian Li, Shuhe Zhao, Hong Yang, Dianmin Cong and Zhaohua Zhang
Land use and cover change (LUCC) is important for the global biogeochemical cycle and ecosystem. This paper introduced a change detection method based on a bi-band binary mask and an improved fuzzy c-means algorithm to research the LUCC. First, the bi-ba...
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Guoliang Feng, Wei Lu and Jianhua Yang
A novel design method for time series modeling and prediction with fuzzy cognitive maps (FCM) is proposed in this paper. The developed model exploits the least square method to learn the weight matrix of FCM derived from the given historical data of time...
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