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Joon-Shik Moon, Chan-Hong Kim and Young-Sang Kim
The advantage of the piezocone penetration test is a guarantee of continuous data, which are a source of reliable interpretation of the target soil layer. Much research has been carried out for several decades, and several classification charts have been...
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Mohamed Shenify, Fokrul Alom Mazarbhuiya and A. S. Wungreiphi
There are many applications of anomaly detection in the Internet of Things domain. IoT technology consists of a large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT d...
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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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Yuhang Tang, Chunxia Liu and Yuechen Li
With the continuous implementation of the rural revitalisation strategy, the social and economic landscape of China?s countryside has undergone significant changes and the structure and functions of rural production space have gradually become more compl...
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Konstantinos Charmanas, Nikolaos Mittas and Lefteris Angelis
Security vulnerabilities constitute one of the most important weaknesses of hardware and software security that can cause severe damage to systems, applications, and users. As a result, software vendors should prioritize the most dangerous and impactful ...
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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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Shiu-Shin Lin, Jheng-Hua Song, Kai-Yang Zhu, Yi-Chuan Liu and Hsien-Cheng Chang
Typhoon intensity forecast is an important issue. The objective of this study is to construct a 5-day 12-hourly typhoon intensity forecast model based on the adaptive neuro-fuzzy inference systems (ANFIS) to improve the typhoon intensity forecast in the ...
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Mahshid Khazaeiathar, Reza Hadizadeh, Nasrin Fathollahzadeh Attar and Britta Schmalz
The behavior of hydrological processes is periodic and stochastic due to the influence of climatic factors. Therefore, it is crucial to develop the models based on their periodicity and stochastic nature for prediction. Furthermore, forecasting the strea...
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Chan-Uk Yeom and Keun-Chang Kwak
In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performan...
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Ramiro Saltos Atiencia, Richard Weber
Pág. 42 - 56
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Mingxian Wang, Hongyan Wang, Langfu Cui, Gang Xiang, Xiaoxuan Han, Qingzhen Zhang and Juan Chen
The aero-engine is the heart of an aircraft; its performance deteriorates rapidly due to the high temperature and high-pressure environment during flights. It is necessary to predict the remaining useful life (RUL) to improve the reliability of aero-engi...
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Shakhnaz Akhmedova, Vladimir Stanovov and Yukihiro Kamiya
In this study, a new approach for novelty and anomaly detection, called HPFuzzNDA, is introduced. It is similar to the Possibilistic Fuzzy multi-class Novelty Detector (PFuzzND), which was originally developed for data streams. Both algorithms initially ...
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Ali Seman and Azizian Mohd Sapawi
In the conventional k-means framework, seeding is the first step toward optimization before the objects are clustered. In random seeding, two main issues arise: the clustering results may be less than optimal and different clustering results may be obtai...
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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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Konstantinos Vantas and Epaminondas Sidiropoulos
The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall?runoff modeling and water resources management. Clustering techniques applied to rainfall dat...
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Fevrier Valdez, Oscar Castillo and Patricia Melin
In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimiz...
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Daniel N. de Bruyn, Ben Kotze and William Hurst
Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding a suitable model to predict energy consumption could prove to be an optimal method to manage the electricity ...
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Jiamin Liu, Yueshi Li, Bin Xiao and Jizong Jiao
The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results...
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Zhiying Tan, Yan Ji, Zhongwen Fei, Xiaobin Xu and Baolai Zhao
Detection of scratch defects on randomly textured surfaces remains challenging due to their unnoticeable visual features. In this paper, an algorithm for piezoelectric ceramic plate surface scratch defects based on the combination of fuzzy c-means cluste...
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