6 research outputs found
A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification
No full textFeature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL
A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification
No full textFeature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL
A novel approach for protein secondary structure prediction using encoder–decoder with attention mechanism model
No full textComputational biology faces many challenges like protein secondary structure prediction (PSS), prediction of solvent accessibility, etc. In this work, we addressed PSS prediction. PSS is based on sequence-structure mapping and interaction among amino acid residues. We proposed an encoder–decoder with an attention mechanism model, which considers the mapping of sequence structure and interaction among residues. The attention mechanism is used to select prominent features from amino acid residues. The proposed model is trained on CB513 and CullPDB open datasets using the Nvidia DGX system. We have tested our proposed method for Q
3 and Q
8 accuracy, segment of overlap, and Mathew correlation coefficient. We achieved 70.63 and 78.93% Q
3 and Q
8 accuracy, respectively, on the CullPDB dataset whereas 79.8 and 77.13% Q
3 and Q
8 accuracy on the CB513 dataset. We observed improvement in SOV up to 80.29 and 91.3% on CullPDB and CB513 datasets. We achieved the results using our proposed model in very few epochs, which is better than the state-of-the-art methods
Biomarker Selection from Gene Expression Data for Tumour Categorization Using Bat Algorithm
No full textExpediency Analysis of Clustering Algorithms for Electric Two-Wheeler Driving Cycle Development Under Indian Smart City Driving Conditions
No full textThe standard driving cycles (DCs) used to evaluate spark-ignition engine-based two-wheelers are inadequate for electric two-wheelers (E2Ws). Also, they fail to accurately represent the actual driving circumstances in specific areas, resulting in inaccuracies during the evaluation of performance. The current research is centred towards constructing an electric two-wheeler urban driving cycle (E2WUDC) that considers the driving circumstances of the smart city in India. Further, the denoised speed data is utilized to extract the micro-trips and compute their driving parameters. Furthermore, the dimensions of the data are decreased through the utilization of principal component analysis. Subsequently, the data is classified utilizing various clustering methods including k-means, X-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN). Then, the Calinski Harabasz index (CHI), Davies-Bouldin index (DBI), and silhouette score are used to assess the homogeneity and completeness of selected clustering algorithms in the data cluster. Overall, the E2WUDC is developed using X-means which is selected as a suitable clustering algorithm based on the performance indices. Also, the key driving features of E2WUDC such as total time duration and distance are 14.49 km and 1914 seconds with average and maximum driving speeds of 8 and 13.88 m/s respectively. Eventually, it establishes the foundation for assessing the energy economy, driving range and energy demand for the widespread deployment of electric two-wheelers in urban commuting
