Abdullah A. Asiri¹, Ahmad Shaf^2,*, Tariq Ali², Muhammad Aamir², Ali Usman², Muhammad Irfan³, Hassan A. Alshamrani¹, Khlood M. Mehdar⁴, Osama M. Alshehri⁵, Samar M. Alqhtani⁶

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 127-143, 2023, DOI:10.32604/iasc.2023.032391

Abstract The brain tumor is an abnormal and hysterical growth of brain tissues, and the leading cause of death affected patients worldwide. Even in this technology-based arena, brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones. The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data. To overcome the highlighted issue, a Generative Adversarial Network (GAN) deep learning technique in which two neural networks… More >

Tao Li¹, Zhiwei Jiang^1,*, Rui Han², Jinyue Xia³, Yongjun Ren⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 941-956, 2023, DOI:10.32604/iasc.2023.029644

Abstract A Generative Adversarial Neural (GAN) network is designed based on deep learning for the Super-Resolution (SR) reconstruction task of temperature fields (comparable to downscaling in the meteorological field), which is limited by the small number of ground stations and the sparse distribution of observations, resulting in a lack of fineness of data. To improve the network’s generalization performance, the residual structure, and batch normalization are used. Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer vision field. Sub-pixel convolution is used instead of transposed convolution or interpolation… More >