Xiaorui Zhang^1,2,3,*, Rui Jiang¹, Wei Sun^3,4, Aiguo Song⁵, Xindong Wei⁶, Ruohan Meng⁷

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.034748

Abstract Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction. Deep learning has extremely powerful in extracting features, and watermarking algorithms based on deep learning have attracted widespread attention. Most existing methods use small kernel convolution to extract image features and embed the watermarking. However, the effective perception fields for small kernel convolution are extremely confined, so the pixels that each watermarking can affect are restricted, thus limiting the performance of the watermarking. To address these problems, we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions. It uses large-kernel… More >

Zoran Gligorić, Miloš Gligorić^*, Igor Miljanović, Suzana Lutovac, Aleksandar Milutinović

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 955-979, 2023, DOI:10.32604/cmes.2023.025021

Abstract Information about the relative importance of each criterion or the weights of criteria can have a significant influence on the ultimate rank of alternatives. Accordingly, assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems. Three methods are commonly used for assessing the weights of criteria: objective, subjective, and integrated methods. In this study, an objective approach is proposed to assess the weights of criteria, called SPC method (Symmetry Point of Criterion). This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence… More > Graphic Abstract

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 >

Amel Ali Alhussan¹, El-Sayed M. El-kenawy^2,3, Hussah Nasser AlEisa^1,*, M. El-SAID^4,5, Sayed A. Ward^6,7, Doaa Sami Khafaga¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2619-2635, 2022, DOI:10.32604/cmc.2022.030445

Abstract Effective technology for wind direction forecasting can be realized using the recent advances in machine learning. Consequently, the stability and safety of power systems are expected to be significantly improved. However, the unstable and unpredictable qualities of the wind predict the wind direction a challenging problem. This paper proposes a practical forecasting approach based on the weighted ensemble of machine learning models. This weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization (PSO-Guided WOA). The proposed optimized weighted ensemble predicts the wind direction given a set of input features. The conducted experiments employed the wind… More >

Letao Gao^1,*, Xiaoming Wang², Tao Wang³, Mengyu Chang⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4741-4754, 2022, DOI:10.32604/cmc.2022.027204

Abstract In the research field of bearing fault diagnosis, classical deep learning models have the problems of too many parameters and high computing cost. In addition, the classical deep learning models are not effective in the scenario of small data. In recent years, deep forest is proposed, which has less hyper parameters and adaptive depth of deep model. In addition, weighted deep forest (WDF) is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision tree. In this paper, weighted deep forest model-based bearing fault diagnosis method (WDBM) is proposed. The WDBM… More >

Harshita Patel¹, Dharmendra Singh Rajput^1,*, Ovidiu Petru Stan², Liviu Cristian Miclea²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 73-89, 2022, DOI:10.32604/cmc.2022.017114

Abstract Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify… More >

Adel Saad Assiri^*

Computer Systems Science and Engineering, Vol.36, No.3, pp. 435-450, 2021, DOI:10.32604/csse.2021.014894

Abstract Artificial neural networks (ANNs) are one of the hottest topics in computer science and artificial intelligence due to their potential and advantages in analyzing real-world problems in various disciplines, including but not limited to physics, biology, chemistry, and engineering. However, ANNs lack several key characteristics of biological neural networks, such as sparsity, scale-freeness, and small-worldness. The concept of sparse and scale-free neural networks has been introduced to fill this gap. Network sparsity is implemented by removing weak weights between neurons during the learning process and replacing them with random weights. When the network is initialized, the neural network is fully… More >

Husam Jasim Mohammed^1,*, Hajem Ati Daham²

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2229-2239, 2021, DOI:10.32604/cmc.2021.014445

Abstract In reality, the flipped classroom has gained popularity as a modern way of structuring teaching, where lectures move from in-class procedures to digitally-based assignments, freeing up the debate, and practice exercises class time. Therefore, it is essential to implement and analyze a way of teaching that will improve student performance. The paper aims to develop a model of the method of teaching science in Iraqi schools, and to assess whether teaching flipped classroom affects the achievement, motivation, and creative thinking of students by using the methodology of Multi-Criteria Decision Making (MCDM) in the Analytic Hierarchy Process (AHP). The AHP approach… More >

Somayeh Ezadi^a, Tofigh Allahviranloo^b

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 193-204, 2018, DOI:10.1080/10798587.2017.1328812

Abstract In this article, the researcher at first focuses on introducing a linear regression based on the Z-number. In this regression, observations are real, but the coefficients and results of observations are unknown and in the form of Z-rating. Therefore, to estimate this type of regression, we have three distinct ways depending on different conditions dominating the problem. The three methods are a combination of artificial neural networks and fuzzy generalized improvements of the technique. Moreover the method of calculating the weights of the Z-number neural network has been mentioned and the stability of neural network weights is considered. In some… More >

Y.F. Nie^1,2, S.N. Atluri², C.W. Zuo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.12, No.2, pp. 137-148, 2006, DOI:10.3970/cmes.2006.012.137

Abstract Owing to the meshless and local characteristics, moving least squares (MLS) methods have been used extensively to approximate the unknown function of partial differential equation initial boundary value problem. In this paper, based on matrix analysis, a sufficient and necessary condition for the existence of inverse of coefficient matrix used in MLS methods is developed firstly. Then in the light of approximate theory, a practical mathematics model is posed to obtain the optimal radius of support of radial weights used in MLS methods. As an example, while uniform distributed particles and the 4^th order spline weight function are adopted in… More >