Mohammed Farsi^*

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 83-92, 2021, DOI:10.32604/iasc.2021.015460 - 17 March 2021

Abstract Microarray cancer data poses many challenges for machine-learning (ML) classification including noisy data, small sample size, high dimensionality, and imbalanced class labels. In this paper, we propose a framework to address these problems by properly utilizing feature-selection techniques. The most important features of the cancer datasets were extracted with Logistic Regression (LR), Chi-2, Random Forest (RF), and LightGBM. These extracted features served as input columns in an applied classification task. This framework’s main advantages are reducing time complexity and the number of irrelevant features for the dataset. For evaluation, the proposed method was compared to… More >

Ehsan Alibagheri¹, Bohayra Mortazavi², Timon Rabczuk^3,4,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1287-1300, 2021, DOI:10.32604/cmc.2021.013564 - 12 January 2021

Abstract Machine-learning (ML) models are novel and robust tools to establish structure-to-property connection on the basis of computationally expensive ab-initio datasets. For advanced technologies, predicting novel materials and identifying their specification are critical issues. Two-dimensional (2D) materials are currently a rapidly growing class which show highly desirable properties for diverse advanced technologies. In this work, our objective is to search for desirable properties, such as the electronic band gap and total energy, among others, for which the accelerated prediction is highly appealing, prior to conducting accurate theoretical and experimental investigations. Among all available componential methods, gradient-boosted More >

Amit Kumar Bhardwaj¹, Lalit Garg^2,*, Arunesh Garg¹, Yuvraj Gajpal³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2471-2492, 2021, DOI:10.32604/cmc.2021.014099 - 28 December 2020

Abstract In the COVID-19 pandemic situation, the need to adopt cloud computing (CC) applications by education institutions, in general, and higher education (HE) institutions, in particular, has especially increased to engage students in an online mode and remotely carrying out research. The adoption of CC across various sectors, including HE, has been picking momentum in the developing countries in the last few years. In the Indian context, the CC adaptation in the HE sector (HES) remains a less thoroughly explored sector, and no comprehensive study is reported in the literature. Therefore, the aim of the present… More >

Dmitry S. Bulgarevich^1,*, Susumu Tsukamoto¹, Tadashi Kasuya², Masahiko Demura¹, Makoto Watanabe^1,3

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 122-122, 2019, DOI:10.32604/icces.2019.05271

Abstract The microstructure of steel greatly determines its mechanical properties/performance and holds information on chemical composition and processing history. Therefore, quantitative analysis of optical or SEM images on formed microstructure phases is one of the primary interests for metallurgy. So far, such analyses in laboratories are done manually by experts and are very time consuming. However, with modern microscopy techniques of automated image acquisitions over the large imaging areas and even by using of sample slicing for three-dimensional imaging, the amount of image data could be overwhelming for manual examinations. In this respect, there is a… More >

A. Kuwata¹, H. Noguchi², H. Kawai³

The International Conference on Computational & Experimental Engineering and Sciences, Vol.2, No.3, pp. 87-92, 2007, DOI:10.3970/icces.2007.002.087

Abstract In this research, we constructed an e-learning system of the CAE (FEM) education. We analyzed about the usefulness of e-learning from each viewpoint of a software developer of the e-learning system, a system administrator of the system, and a user. In addition, we considered about the necessary technology in Web application. More >