Md. Munirul Hasan¹, Md Mustafizur Rahman^2,*, Mohammad Saiful Islam³, Wong Hung Chan⁴, Yasser M. Alginahi⁵, Muhammad Nomani Kabir⁶, Suraya Abu Bakar¹, Devarajan Ramasamy²

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 537-556, 2024, DOI:10.32604/fhmt.2024.047428 - 20 May 2024

Abstract A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range. In most radiators that are used to cool an engine, water serves as a cooling fluid. The performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water. Concentration and uniformity of nanoparticle distribution are the two major factors for the practical use of nanofluids. The shape and size of nanoparticles also have a great impact on the performance of heat transfer. Many researchers are… More > Graphic Abstract

G. Naveen Sundar¹, Stalin Selvaraj², D. Narmadha¹, K. Martin Sagayam³, A. Amir Anton Jone³, Ayman A. Aly⁴, Dac-Nhuong Le^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 31-46, 2022, DOI:10.32604/cmes.2022.019914 - 18 July 2022

Abstract Hepatocellular carcinoma (HCC) is one major cause of cancer-related mortality around the world. However, at advanced stages of HCC, systematic treatment options are currently limited. As a result, new pharmacological targets must be discovered regularly, and then tailored medicines against HCC must be developed. In this research, we used biomarkers of HCC to collect the protein interaction network related to HCC. Initially, DC (Degree Centrality) was employed to assess the importance of each protein. Then an improved Graph Coloring algorithm was used to rank the target proteins according to the interaction with the primary target More >

Yiping Sun^1,2, Qiang Bai¹, Xuefeng Zhao¹, Meng Tao^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1149-1170, 2022, DOI:10.32604/cmes.2022.017760 - 13 December 2021

Abstract A cavity viscoelastic structure has a good sound absorption performance and is often used as a reflective baffle or sound absorption cover in underwater acoustic structures. The acoustic performance field has become a key research direction worldwide. Because of the time-consuming shortcomings of the traditional numerical analysis method and the high cost of the experimental method for measuring the reflection coefficient to evaluate the acoustic performance of coatings, this innovative study predicted the reflection coefficient of a viscoelastic coating containing a cylindrical cavity based on an artificial neural network (ANN). First, the mapping relationship between… More >

Phyu Hnin Thike^{1, 2}, Zhaoyang Zhao¹, Peng Liu¹, Feihu Bao¹, Ying Jin¹, Peng Shi^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2091-2109, 2020, DOI:10.32604/cmc.2020.011608 - 16 September 2020

Abstract The optimization of network topologies to retain the generalization ability by deciding when to stop overtraining an artificial neural network (ANN) is an existing vital challenge in ANN prediction works. The larger the dataset the ANN is trained with, the better generalization the prediction can give. In this paper, a large dataset of atmospheric corrosion data of carbon steel compiled from several resources is used to train and test a multilayer backpropagation ANN model as well as two conventional corrosion prediction models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning… More >

S.A. Yıldızel^1,2, A.U. Öztürk¹

CMC-Computers, Materials & Continua, Vol.52, No.1, pp. 41-52, 2016, DOI:10.3970/cmc.2016.052.041

Abstract In this study, artificial neural networks trained with swarm based artificial bee colony optimization algorithm was implemented for prediction of the modulus of rapture values of the fabricated glass fiber reinforced concrete panels. For the application of the ANN models, 143 different four-point bending test results of glass fiber reinforced concrete mixes with the varied parameters of temperature, fiber content and slump values were introduced the artificial bee colony optimization and conventional back propagation algorithms. Training and the testing results of the corresponding models showed that artificial neural networks trained with the artificial bee colony More >