Naif Alotaibi¹, A. S. Al-Moisheer², Ibrahim Elbatal¹, Mohammed Elgarhy^3,4, Ehab M. Almetwally^1,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2795-2823, 2024, DOI:10.32604/cmes.2024.049188

Abstract This article introduces a novel variant of the generalized linear exponential (GLE) distribution, known as the sine generalized linear exponential (SGLE) distribution. The SGLE distribution utilizes the sine transformation to enhance its capabilities. The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues. The suggested model incorporates a hazard rate function (HRF) that may display a rising, J-shaped, or bathtub form, depending on its unique characteristics. This model includes many well-known lifespan distributions as separate sub-models. The suggested model is accompanied with a range of More >

Zohaib Ahmad Khan¹, Yuanqing Xia^1,*, Ahmed Khan², Muhammad Sadiq², Mahmood Alam³, Fuad A. Awwad⁴, Emad A. A. Ismail⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2771-2793, 2024, DOI:10.32604/cmc.2024.046897

Abstract Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis.… More >

Alessandro Araldi¹, David Emsellem², Giovanni Fusco¹, Andrea Tettamanzi³, Denis Overal²

Revue Internationale de Géomatique, Vol.31, No.2, pp. 265-302, 2022, DOI:10.3166/RIG.31.265-302©2022

Abstract The identification and description of building typologies play a fundamental role in the understanding of the overall built-up form. A growing body of research is developing and implementing sophisticated, computer-aided protocols for the identification of building typologies. This paper shares the same goal. An innovative data-driven procedure for the unsupervised identification and description of building types and organization is here presented. After a specific pre-processing procedure, we develop an unsupervised clustering combining a new algorithm of Naive Bayes inference and hierarchical ascendant approaches relying on six morphometric features of buildings. This protocol allows us to More >

N. Gnanasekaran, C. Balaji^*

Frontiers in Heat and Mass Transfer, Vol.2, No.2, pp. 1-6, 2011, DOI:10.5098/hmt.v2.2.3008

Abstract This paper reports the results of an experimental investigation of transient, turbulent mixed convection in a vertical channel in which one of the walls is heated and the other is adiabatic. The goal is to simultaneously estimate the constants in a Nusselt number correlation whose form is assumed a priori by synergistically marrying the experimental results with repeated numerical calculations that assume guess values of the constants. The convective heat transfer coefficient “h”is replaced by the Nusselt number (Nu) and the constants in the Nusselt number are to be evaluated. From the experimentally obtained temperature More >

Shahad Alzahrani¹, Hatim Alsuwat², Emad Alsuwat^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718

Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework… More >

Refah Alotaibi¹, Mazen Nassar^2,3, Ahmed Elshahhat^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2571-2596, 2024, DOI:10.32604/cmes.2023.030582

Abstract A novel extended Lindley lifetime model that exhibits unimodal or decreasing density shapes as well as increasing, bathtub or unimodal-then-bathtub failure rates, named the Marshall-Olkin-Lindley (MOL) model is studied. In this research, using a progressive Type-II censored, various inferences of the MOL model parameters of life are introduced. Utilizing the maximum likelihood method as a classical approach, the estimators of the model parameters and various reliability measures are investigated. Against both symmetric and asymmetric loss functions, the Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) technique with the assumption of independent gamma… More >

Qing Xia¹, Yonghua Li^2,*, Dongxu Zhang², Yufeng Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1769-1794, 2024, DOI:10.32604/cmes.2023.030724

Abstract For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network… More >

Kun Liu^1,2, Peiran Li³, Yu Zhang^1,*, Jia Ren¹, Xianyu Wang², Uzair Aslam Bhatti¹

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3257-3274, 2023, DOI:10.32604/cmc.2023.039430

Abstract To obtain the optimal Bayesian network (BN) structure, researchers often use the hybrid learning algorithm that combines the constraint-based (CB) method and the score-and-search (SS) method. This hybrid method has the problem that the search efficiency could be improved due to the ample search space. The search process quickly falls into the local optimal solution, unable to obtain the global optimal. Based on this, the Particle Swarm Optimization (PSO) algorithm based on the search space constraint process is proposed. In the first stage, the method uses dynamic adjustment factors to constrain the structure search space… More >

Shoutao Li^1,2,*, Ruyi Qu¹, Yu Zhang¹, Dingli Yu³

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2809-2823, 2023, DOI:10.32604/iasc.2023.036999

Abstract Freezing of Gait (FOG) is the most common and disabling gait disorder in patients with Parkinson’s Disease (PD), which seriously affects the life quality and social function of patients. This paper proposes a FOG recognition method based on the Variational Mode Decomposition (VMD). Firstly, VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal. Secondly, to improve the accuracy and speed of the recognition algorithm, use the CART model as the base classifier and perform the feature dimension reduction. Then use the RUSBoost ensemble algorithm to solve the problem… More >

Dan Zhang¹, Yu Zhang², Yiwen Liang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2317-2336, 2023, DOI:10.32604/cmc.2023.038026

Abstract The dendritic cell algorithm (DCA) is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system. Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA. The loss function of DCA is ambiguous due to its complexity. To reduce the uncertainty, several researchers simplified the algorithm program; some introduced gradient descent to optimize parameters; some utilized searching methods to find the optimal parameter combination. However, these studies are either time-consuming or need to be revised in the case of non-convex… More >