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Dr. Lokanayaki K

Dr. Lokanayaki K

Associate Professor

About

Lokanayaki K is an Associate Professor at School of Computer Science and Engineering, RV University. She holds Doctoral Degree (Ph.D.) in Computer Science from Bharathiar University, with research focusing on developing advanced methods for liver cancer analysis using ensemble sliding and swarm-based approaches. She has 18 years of teaching experience in various educational institutions. She has been involved in several research projects and has published extensively in international journals and conferences.Additionally, she has obtained patents in fields such as industrial cyber-physical systems and cognitive internet of things. She is also a member of multiple professional bodies and has attended numerous workshops and faculty development programs to enhance her teaching and research skill

Success is not final, failure is not fatal: It is the courage to continue that counts.

Enhanced Artificial Bee Colony Algorithm for Liver Cancer Analysis
ABSTRACT During the development of computer technology, computer-aided diagnosis (CAD) technology, used in quantitative analysis of medical imaging, arose at a historic moment and became a research hotspot in medical imaging. Discrimination of hepatocellular carcinoma (HCC) in the liver is a challenge in the histopathologic diagnostics. For this reason, there is an urgent need for new detection methods to improve the accuracy of the diagnosis of liver cancer. Traditional machine-learning approaches are neural network (NN)-based. Cost-sensitive learning and a support vector machine (SVM) is observed to provide a good result in the case of balanced data sets; however, it is not capable of dealing with the classification of imbalanced data sets. These machine-learning approaches may be biased toward the majority class, thus producing a poor predictive accuracy over the minority class. In this paper, a novel technique for the purpose of liver cancer cell classification and root liver cancer cell recognition is proposed. The objective is to automatically categorize several classes of liver cancer cells and to discover the root cancer cell. To solve this problem, initially, preprocessing on noisy imbalanced data sets is carried out by means of improved weighted synthetic minority oversampling technique (IWSMOTE)-based oversampling and evolutionary undersampling. An ensemble-based learning algorithm (DataBoost.IM) with SVM is employed for final classification to classify the cancer cells and non- cancer cells. Finally, the enhanced artificial bee colony (EABC) clustering is applied to discover the root cancer cell. The proposed EABC clustering approach is tested using the liver cancer cell data set, providing an accuracy level of 96.15 %, which is 95.61 % and 92.80 % higher than the ant colony optimization (ACO) and artificial bee colony (ABC) algorithms, respectively.
Enhanced Artificial Bee Colony Algorithm for Liver Cancer Analysis

Link: https://www.astm.org/jte20160411.html

Patents: Granted multiple patents, including those related to early detection of cyber-physical system attacks and optimization algorithms for cognitive IoT.

Conference Publications: Presented research on topics such as hybrid deep learning for cloud intrusion detection and swarm optimization methods for liver cancer datasets.

Journal Publications: Authored several papers in journals, including those indexed in SCOPUS and SCI, covering areas like cloud intrusion detection and enhanced algorithms for medical data analysis.

Research Projects: Executed projects on intrusion detection systems using artificial bee colony optimization.

Research Interests

Artificial Intelligence, Deep Learning, Network Security

Dr. APJ Abdul Kalam Lifetime Achievement National Award By National Institute for Socio economic Development.

Dr. Lokanayaki K

Associate Professor

M.Sc.,M.Phil., PhD

School of Computer Science and Engineering

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