Nana Zhang¹, Kun Zhu¹, Shi Ying^{1, *}, Xu Wang²

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 471-499, 2020, DOI:10.32604/cmc.2020.010117

Abstract Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis (KPCA), adaptive genetic algorithm, extreme… More >

Tao Li¹, Xiang Li^{1, *}, Yongjun Ren², Jinyue Xia³

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 621-631, 2020, DOI:10.32604/cmc.2020.06463

Abstract In view of the low accuracy of traditional ground nephogram recognition model, the authors put forward a k-means algorithm-acquired neural network ensemble method, which takes BP neural network ensemble model as the basis, uses k-means algorithm to choose the individual neural networks with partial diversities for integration, and builds the cloud form classification model. Through simulation experiments on ground nephogram samples, the results show that the algorithm proposed in the article can effectively improve the Classification accuracy of ground nephogram recognition in comparison with applying single BP neural network and traditional BP AdaBoost ensemble algorithm on classification of ground nephogram. More >

Haijian Shao^{1, 2}, Xing Deng^{1, 2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.3, pp. 277-293, 2018, DOI:10.3970/cmes.2018.114.277

Abstract High accurary in wind speed forcasting remains hard to achieve due to wind’s random distribution nature and its seasonal characteristics. Randomness, intermittent and nonstationary usually cause the portion problem of the wind speed forecasting. Seasonal characteristics of wind speed means that its feature distribution is inconsistent. This typically results that the persistence of excitation for modeling can not be guaranteed, and may severely reduce the possibilities of high precise forecasting model. In this paper, we proposed two effective solutions to solve the problems caused by the randomness and seasonal characteristics of the wind speed. (1) Wavelet analysis is used to… More >

B. V. Santhosh Krishna^{1, *}, T. Gnanasekaran²

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 855-869, 2019, DOI:10.32604/cmc.2019.07585

Abstract An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction… More >