Lihong Guo^*, Jie Yang, Haitao Wu

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1929-1946, 2023, DOI:10.32604/cmc.2023.039170

Abstract Attribute-based encryption (ABE) is a technique used to encrypt data, it has the flexibility of access control, high security, and resistance to collusion attacks, and especially it is used in cloud security protection. However, a large number of bilinear mappings are used in ABE, and the calculation of bilinear pairing is time-consuming. So there is the problem of low efficiency. On the other hand, the decryption key is not uniquely associated with personal identification information, if the decryption key is maliciously sold, ABE is unable to achieve accountability for the user. In practical applications, shared message requires hierarchical sharing in… More >

Munawar Hassan¹, Shafqat Iqbal², Harish Garg^3,*, Shahbaz Gul Hassan⁴, Yunxian Yan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 559-584, 2023, DOI:10.32604/cmes.2022.021385

Abstract The main objective of this paper is to present an integrated approach to evaluate the level of satisfaction of borrowers with the products and services of microfinance institutions (MFI) at different criterion levels. For this, the study adopts the concept of FCEM (Fuzzy Comprehensive Evaluation Method) in concurrence with the AHP (Analytical Hierarchy Process). In our day-to-day situation, the researchers have made many efforts to assess the impact of Microfinance on poverty reduction, but borrowers’ satisfaction is always overlooked. Since the multiple factors impact the borrower’s satisfaction, each factor is made of different items. Thus, as the factors items increase,… More >

Lee-Kien Foo^*, Sook-Ling Chua, Neveen Ibrahim

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1945-1957, 2022, DOI:10.32604/cmc.2022.022011

Abstract The naïve Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naïve Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time. One approach… More >