Yani Feng¹, Kejun Tang², Lianxing He³, Pingqiang Zhou¹, Qifeng Liao^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1195-1218, 2023, DOI:10.32604/cmes.2022.021636

Abstract This work proposes a Tensor Train Random Projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved. Our TTRP is systematically constructed through a Tensor Train (TT) representation with TT-ranks equal to one. Based on the tensor train format, this random projection method can speed up the dimension reduction procedure for high-dimensional datasets and requires fewer storage costs with little loss in accuracy, compared with existing methods. We provide a theoretical analysis of the bias and the variance of TTRP, which shows that this approach is an expected isometric projection with bounded variance, and we show that… More >

Mayada Tarek^1,2,*

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2049-2063, 2023, DOI:10.32604/csse.2023.027139

Abstract Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system’s database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction with the biometric data. The… More >

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, Fatimah Algarni⁴, Amel A. Alhussan⁵, Fathi E. Abd El-Samie^1,2

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1881-1891, 2022, DOI:10.32604/iasc.2022.017683

Abstract Recently, deep learning has opened a remarkable research direction in the track of bioinformatics, especially for the applications that need classification and regression. With deep learning techniques, DNA sequences can be classified with high accuracy. Firstly, a DNA sequence should be represented, numerically. After that, DNA features are extracted from the numerical representations based on deep learning techniques to improve the classification process. Recently, several architectures have been developed based on deep learning for DNA sequence classification. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are the default deep learning architectures used for this task. This paper presents a… More >