2022-04

2022 International Conference on Computer Science and Software Engineering (CSASE)

The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection (FS). However, the increasing feature dimensions and small sample size in microarray datasets pose a significant challenge to most existing algorithms. To overcome this issue, we propose a novel method based on Principle Component Analysis (PCA) and Cuttlefish Algorithm (CFA), which is a recent bio-inspired feature selection algorithm. The critical characteristic of the PCA algorithm is that it is less sensitive to noise and requires less memory and capacity. Furthermore, adopting the PCA approach before using CFA minimises the search space within CFA, which speeds up determining the best subset of features while reducing the computational cost. To assess the performance of the proposed method, three publicly available microarray datasets are utilized in the experimental studies using a Linear Discriminant Analysis classifier. Experimental results showed that PCA with CFA significantly outperforms the state-of-art feature selection methods.

2022-04

2022 International Conference on Computer Science and Software Engineering (CSASE)

The redundant or irrelevant features in microarray datasets cause difficulty in apprehending the prospect patterns directly and accurately. One of the necessary strategies for distinguishing and screening out the most relevant features is Feature Selection (FS). However, the increasing feature dimensions and small sample size in microarray datasets pose a significant challenge to most existing algorithms. To overcome this issue, we propose a novel method based on Principle Component Analysis (PCA) and Cuttlefish Algorithm (CFA), which is a recent bio-inspired feature selection algorithm. The critical characteristic of the PCA algorithm is that it is less sensitive to noise and requires less memory and capacity. Furthermore, adopting the PCA approach before using CFA minimises the search space within CFA, which speeds up determining the best subset of features while reducing the computational cost. To assess the performance of the proposed method, three publicly available microarray datasets are utilized in the experimental studies using a Linear Discriminant Analysis classifier. Experimental results showed that PCA with CFA significantly outperforms the state-of-art feature selection methods.

2018-11

2018 International Conference on Advanced Science and Engineering (ICOASE)

Concealing confidential messages within DNA sequences has turned into a well-known research in latest years. This paper presents a modified scheme which is based on the Table Lookup Substitution Method (TLSM) to increase its security. The proposed scheme uses an 8-bit binary coding to transform a reference DNA sequence into a binary format to increase the security of the original TLSM. A comparison between the proposed modified scheme with the existing schemes besides the original TLSM is presented. The results and comparisons have proven the ability of the proposed scheme in balancing among the three critical properties for any DNA steganography scheme: capacity, payload, and BPN. In addition, the cracking probability of the proposed modified scheme is more complex than the original TLSM.