Nageswara Rao Eluri
@rvrjc.ac.in
Associate Professor, Department of CSE(IOT)
RVR&JC College of Engineering
EDUCATION
Ph.D. CSE From Acharya Nagarjuna University
RESEARCH INTERESTS
Machine Learning, Deep Learning, Computer Security and Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Nageswara Rao Eluri, Gangadhara Rao Kancharla, Suresh Dara, and Venkatesulu Dondeti
Emerald
PurposeGene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.Design/methodology/approachThe proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.FindingsThe proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.Originality/valueThis paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.
Koduganti Venkata Rao, B. Prasanth Kumar, Ch. Viswanadh Sharma, Nageswara Rao Eluri, and Beesetti Kiran Kumar
Springer Singapore
Suresh Dara, Mamidi Jagadeeshwara Reddy, and Nageswara Rao Eluri
IEEE
In previous years, different Lateral thinking optimization techniques have been developed based on evolutionary computation. Many of these methods are inspired by spill out behaviors in nature. In this Paper, a new optimization algorithm based on the law of gravity and mass interactions named as Gravitational Search Algorithm (GSA) is discussed for solving feature selection. In GSA, the searcher agents are a collection of masses which will interact with each other based on the law of motion and Newtonian gravity which gives the binary evolutionary optimized high performance. The detailed feature selection has been discussed in this paper and The GSA method has been compared with some well-known optimized search methods such as GA (Genetic Algorithm), PSO(Particle Swarm Optimization).