Tirumala Krishna Battula

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA, ELECTRONICS AND COMMUNICATION ENGINEERING, Faculty Member

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B. Tirumala Krishna completed his B.E., M.E., and Ph.D. degrees at the College of Engineering, Andhra University, Visakhapatnam, India. He worked at GITAM University, Visakhapatnam, India, at various levels as Assistant Professor and Senior Assistant Professor.He is currently working as Professor at the Department of ECE, Jawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada, India. His research areas of interest are fractional-order systems, fractional-order signal processing, and analog VLSI. His Bio Data is placed in Marquis who’s who in the world in 2014, 15, 16 editions. He is a member of IEEE, Fellow of Institute of Electronics and Telecommunication Engineers (FIETE), Life Member Instrument Society of India, Life Member Bio Medical Engineering Society of India. One of his papers is listed in best downloaded, Best cited articles from Elsevier signal processing journal during 2011, 2012. He has visited Srilanka, Indonesia, Malaysia and Thailand to present paper, chairing technical sessions in various conferences. He has reviewed papers in many international journals of IEEE (USA), Elsevier Journals, Springer Journals and some Conferences. He is currently handling two sponsored research projects each from Department of Science and Technology (DST), New Delhi and University Grants Commission, New Delhi, India.

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IIIT Bhubaneswar

Universiti Tun Hussein Onn Malaysia

Thaer Thaher

Prof. Dr. Essam H . Houssein

Hamouda Chantar

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Papers by Tirumala Krishna Battula

AbinaryBiphasemutationbasedhybridEquilibriumOptimizerfor featureselectioninmedicaldatasetsclassification

With the rapid expansion in Biological Sciences, biomedical data classification has become challe... more With the rapid expansion in Biological Sciences, biomedical data classification has become challenging. These datasets generally consist of missing values, redundant features and irrelevant information. A novel hybrid wrapper-based feature selection method is proposed to tackle these issues effectively. In order to improve the exploration ability of the particles, the Sine factor is integrated with the Equilibrium Optimizer (EO) technique. A Bi-phase Mutation (BM) scheme is integrated to enhance the exploitation phase of the EO algorithm (BM-based Hybrid EO, BMHEO). The BMHEO method is evaluated by employing four different classifiers -KNN, SVM, Random Forest (RF) and Discriminant Analysis (DA). It is observed that the Random Forest classifier exhibits superior performance compared to the other three classifiers. Eight S-shaped and V-shaped transfer functions are integrated to convert the solutions to binary form. Based on the above enhancements, eight different versions of BMHEO are produced. The performance of these versions is assessed using twenty biomedical datasets. Experiments on these datasets demonstrate that the BMHEO-S2 version outperforms other methods in terms of fitness values and classification accuracies. This approach is also tested on a MED-NODE image dataset for classification, and higher accuracy of 97.02% is achieved.

Tirumala Krishna Battula

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