Taiyu Yan¹, Yuxin Cao¹, Guoxia Xu¹, Xiaoran Zhao², Hu Zhu¹, Lizhen Deng^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3741-3764, 2023, DOI:10.32604/cmc.2023.039828 - 26 December 2023

Abstract Unmanned Aerial Vehicle (UAV) tracking has been possible because of the growth of intelligent information technology in smart cities, making it simple to gather data at any time by dynamically monitoring events, people, the environment, and other aspects in the city. The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations. But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms. In light of the aforementioned issues, this work suggests a dual-domain Jensen-Shannon divergence… More >

Pham Viet Anh^1,3, Nguyen Ngoc Thuy⁴, Nguyen Long Giang², Pham Dinh Khanh⁵, Nguyen The Thuy^1,6,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2971-2988, 2023, DOI:10.32604/csse.2023.042068 - 09 November 2023

Abstract Attribute reduction, also known as feature selection, for decision information systems is one of the most pivotal issues in machine learning and data mining. Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problem of attribute reduction. Unfortunately, the intuitionistic fuzzy sets based methods have not received much interest, while these methods are well-known as a very powerful approach to noisy decision tables, i.e., data tables with the low initial classification accuracy. Therefore, this paper provides a novel incremental attribute reduction method to deal more… More >

Wenshu Xu, Mingwei Lin^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 199-222, 2023, DOI:10.32604/cmc.2023.041475 - 31 October 2023

Abstract Industrial control systems (ICSs) are widely used in various fields, and the information security problems of ICSs are increasingly serious. The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts. Thus, this paper introduces the probabilistic linguistic term sets (PLTSs) to model the evaluation information of experts. Meanwhile, we propose a probabilistic linguistic multi-criteria decision-making (PL-MCDM) method to solve the information security assessment problem of ICSs. Firstly, we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods. Secondly, we use the Best Worst… More >

Idris Rabiu^1,3,*, Naomie Salim², Maged Nasser^1,4, Aminu Da’u¹, Taiseer Abdalla Elfadil Eisa⁵, Mhassen Elnour Elneel Dalam⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6001-6017, 2023, DOI:10.32604/cmc.2023.035221 - 28 December 2022

Abstract Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products. Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers. The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections. To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams. This paper presents… More >

Fangwei Zhang^1,2, Yi Zhao^3,*, Jun Ye⁴, Shuhong Wang⁵, Jingyi Hu⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1589-1602, 2023, DOI:10.32604/cmes.2022.022431 - 27 October 2022

Abstract The purpose of this study is to reduce the uncertainty in the calculation process on hesitant fuzzy sets (HFSs). The innovation of this study is to unify the cardinal numbers of hesitant fuzzy elements (HFEs) in a special way. Firstly, a probability density function is assigned for any given HFE. Thereafter, equal-probability transformation is introduced to transform HFEs with different cardinal numbers on the condition into the same probability density function. The characteristic of this transformation is that the higher the consistency of the membership degrees in HFEs, the higher the credibility of the mentioned More >

Muhammad Riaz¹, Harish Garg², Hafiz Muhammad Athar Farid¹, Ronnason Chinram^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 771-800, 2021, DOI:10.32604/cmes.2021.014174 - 19 April 2021

Abstract This paper aims to introduce the novel concept of the bipolar picture fuzzy set (BPFS) as a hybrid structure of bipolar fuzzy set (BFS) and picture fuzzy set (PFS). BPFS is a new kind of fuzzy sets to deal with bipolarity (both positive and negative aspects) to each membership degree (belonging-ness), neutral membership (not decided), and non-membership degree (refusal). In this article, some basic properties of bipolar picture fuzzy sets (BPFSs) and their fundamental operations are introduced. The score function, accuracy function and certainty function are suggested to discuss the comparability of bipolar picture fuzzy More >

Mingming Yang^*, Junchuan Yang

Journal of New Media, Vol.3, No.1, pp. 19-27, 2021, DOI:10.32604/jnm.2021.018267 - 15 March 2021

Abstract Every day we receive a large amount of information through different social media and software, and this data and information can be realized with the advent of data mining methods. In the process of data mining, to solve some high-dimensional problems, feature selection is carried out in limited training samples, and effective features are selected. This paper focuses on two Relief feature selection algorithms: Relief and ReliefF algorithm. The differences between them and their respective applicable scopes are analyzed. Based on Relief algorithm, the high weight feature subset is obtained, and the correlation between features More >

Marta Mieczyńska^1,*, Ireneusz Czarnowski²

Computer Systems Science and Engineering, Vol.36, No.1, pp. 69-82, 2021, DOI:10.32604/csse.2021.014327 - 23 December 2020

Abstract Automatic Identification System (AIS) data stream analysis is based on the AIS data of different vessel’s behaviours, including the vessels’ routes. When the AIS data consists of outliers, noises, or are incomplete, then the analysis of the vessel’s behaviours is not possible or is limited. When the data consists of outliers, it is not possible to automatically assign the AIS data to a particular vessel. In this paper, a clustering method is proposed to support the AIS data analysis, to qualify noises and outliers with respect to their suitability, and finally to aid the reconstruction… More >