Sanghyuk Lee¹, Eunmi Lee^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.070504 - 09 December 2025

Abstract Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information. This study introduces a refined approach to addressing uncertainty in decision-making, employing existing measures used in decision problems. Building on information theory, the Kullback–Leibler (KL) divergence is extended to incorporate additional insights, specifically by applying temporal data, as illustrated by time series data from two datasets (e.g., affirmative and dissent information). Cumulative hesitation provides quantifiable insights into the decision-making process. Accordingly, a modified KL divergence, which incorporates historical trends, is proposed, enabling dynamic updates using conditional More >

Harsh Mankodiya¹, Priyal Palkhiwala¹, Rajesh Gupta^1,*, Nilesh Kumar Jadav¹, Sudeep Tanwar¹, Osama Alfarraj², Amr Tolba², Maria Simona Raboaca^3,4,*, Verdes Marina⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1123-1142, 2023, DOI:10.32604/cmc.2023.038556 - 31 October 2023

Abstract The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework More >

S. Maheswari, R. Vijayabhasker^*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3677-3692, 2023, DOI:10.32604/iasc.2023.031422 - 17 August 2022

Abstract Mobile Ad-hoc Networks (MANET) usage across the globe is increasing by the day. Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes. The trust values are estimated based on the reputation values of each node in the network by using different mechanisms. However, these mechanisms have various challenging issues which degrade the network performance. Hence, a novel Quality of Service (QoS) Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work. Initially, the QoS-based trust estimation is proposed by using a… 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 - 03 November 2021

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… More >