Phi-Hung Nguyen^1,2,*, Thanh-Tuan Dang³, Kim-Anh Nguyen¹, Hong-Anh Pham¹

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2055-2075, 2022, DOI:10.32604/cmc.2022.025532

Abstract In international trade, exporters prefer to receive payments as quickly as possible, and importers want to make payments as late as possible. In this respect, the payment field, an essential condition for trade transactions, also represents the positions of exporters and importers conflict. In addition, there are many cases in which various variables must be considered rather than only one specific variable representatively affecting payment, particularly in the case of import-export Small and Medium-Sized Enterprises (SMEs) from emerging countries. A selection of proper payment methods can be categorized as a Multi-Criteria Decision-Making (MCDM) issue. Therefore, this study aims to propose… More >

Jing Fu^1,2, Jun Ye³, Liping Xie^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1751-1769, 2022, DOI:10.32604/cmes.2022.018739

Abstract Since existing selection methods of surgical treatment schemes of renal cancer patients mainly depend on physicians’ clinical experience and judgments, the surgical treatment options of renal cancer patients lack their scientifical and reasonable information expression and group decision-making model for renal cancer patients. Fuzzy multi-sets (FMSs) have a number of properties, which make them suitable for expressing the uncertain information of medical diagnoses and treatments in group decision-making (GDM) problems. To choose the most appropriate surgical treatment scheme for a patient with localized renal cell carcinoma (RCC) (T1 stage kidney tumor), this article needs to develop an effective GDM model… More >

D. Ajay¹, J. Aldring¹, G. Rajchakit², P. Hammachukiattikul³, N. Boonsatit^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1033-1076, 2022, DOI:10.32604/cmes.2022.018267

Abstract In this paper, sine trigonometry operational laws (ST-OLs) have been extended to neutrosophic sets (NSs) and the operations and functionality of these laws are studied. Then, extending these ST-OLs to complex neutrosophic sets (CNSs) forms the core of this work. Some of the mathematical properties are proved based on ST-OLs. Fundamental operations and the distance measures between complex neutrosophic numbers (CNNs) based on the ST-OLs are discussed with numerical illustrations. Further the arithmetic and geometric aggregation operators are established and their properties are verified with numerical data. The general properties of the developed sine trigonometry weighted averaging/geometric aggregation operators for… More >

Walid Aydi^1,3,*, Fuad S. Alduais^2,4

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4033-4050, 2022, DOI:10.32604/cmc.2022.023119

Abstract The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers, and eases… More >

Alaa Eisa¹, Nora EL-Rashidy², Mohammad Dahman Alshehri^3,*, Hazem M. El-bakry¹, Samir Abdelrazek¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2901-2921, 2022, DOI:10.32604/cmc.2022.021342

Abstract At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant… More >

Ali Algarni¹, Mahmoud Ragab^2,3,4,*, Wardah Alamri⁵, Samih M. Mostafa⁶

Computer Systems Science and Engineering, Vol.42, No.1, pp. 303-318, 2022, DOI:10.32604/csse.2022.022152

Abstract The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be… More >

Zhongfu Tan^1,2, Jing Wang^1,*, Caixia Tan¹, Gejirifu De^3,4

Energy Engineering, Vol.119, No.1, pp. 331-357, 2022, DOI:10.32604/EE.2022.015700

Abstract At present, there are few studies on the comprehensive evaluation of green power grid development in China, and all aspects of green power grid need to be evaluated. Therefore, this paper studies the green development level of power distribution network. This paper proposes a multi-level fuzzy comprehensive evaluation method, which first needs to classify the influencing factors. Therefore, this paper constructs an indicator system for the evaluation of green development of power distribution network from three dimensions. In order to avoid the influence of subjective factors, this paper adopts the model combining analytic hierarchy process and entropy weight method to… More >

S. Premkumar Deepak^*, M. B. Mukeshkrishnan

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 963-978, 2022, DOI:10.32604/iasc.2022.022126

Abstract Underwater wireless sensor networks (UWSNs) are promising, emerging technologies for the applications in oceanic research. UWSN contains high number of sensor nodes and autonomous underwater vehicles that are deployed to perform the data transmission in the sea. In UWSN networks, the sensors are placed in the buoyant which are highly vulnerable to selfish behavioural attack. In this paper, the major challenges in finding secure and optimal route navigation in UWSN are identified and in order to address them, Entropy based ACO algorithm (E-ACO) is proposed for secure route selection. Moreover, the Selfish Node Recovery (SNR) using the Grasshopper Optimisation Algorithm… More >

T. Gopalakrishnan¹, Periakaruppan Sudhakaran², K. C. Ramya³, K. Sathesh Kumar⁴, Fahd N. Al-Wesabi^5,6,*, Manal Abdullah Alohali⁷, Anwer Mustafa Hilal⁸

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 305-320, 2022, DOI:10.32604/cmc.2022.020914

Abstract Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such as muscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging (MRI). Among these techniques, Muscle MRI recommends the diagnosis of muscular dystrophy through identification of the patterns that exist in muscle fatty replacement. But the patterns overlap among various diseases whereas there is a lack of knowledge prevalent with regards to disease-specific patterns. Therefore, artificial intelligence techniques can be used in the diagnosis of muscular dystrophies, which enables us to analyze, learn, and predict for the future. In this scenario,… More >

Muhammad Ibrahim¹, Muhammad Hanif², Shabir Ahmad³, Faisal Jamil¹, Tayyaba Sehar², YunJung Lee⁴, DoHyeun Kim^1,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5651-5665, 2022, DOI:10.32604/cmc.2022.017772

Abstract The Internet of Things (IoT) has been widely adopted in various domains including smart cities, healthcare, smart factories, etc. In the last few years, the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms. The IoT fitness devices collect trainee data that is being used for various decision-making. However, it will face numerous security and privacy issues towards its realization. This work focuses on IoT security, especially DoS/DDoS attacks. In this paper, we have proposed a novel blockchain-enabled protocol (BEP) that uses the notion of a self-exposing node (SEN) approach… More >