Naglaa. F. Soliman^1,*, Samia M. Abd-Alhalem² , Walid El-Shafai² , Salah Eldin S. E. Abdulrahman³, N. Ismaiel³ , El-Sayed M. El-Rabaie² , Abeer D. Algarni¹, Fathi E. Abd El-Samie^1,2

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5907-5927, 2022, DOI:10.32604/cmc.2022.018860 - 11 October 2021

Abstract

Recently, deep learning (DL) became one of the essential tools in bioinformatics. A modified convolutional neural network (CNN) is employed in this paper for building an integrated model for deoxyribonucleic acid (DNA) classification. In any CNN model, convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features. A novel method based on downsampling and CNNs is introduced for feature reduction. The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy. The two-dimensional discrete transform (2D DT) and two-dimensional random projection (2D RP) methods

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Jingming Xia¹, Yi Lu¹, Ling Tan^2,*, Ping Jiang³

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 613-624, 2021, DOI:10.32604/cmc.2021.013457 - 12 January 2021

Abstract Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse More >