V. Kakulapati

Work place: Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India

E-mail: vldms@yahoo.com

Website: https://orcid.org/0000-0002-1753-3298

Research Interests: Health Informatics, Artificial Intelligence, Big Data Analytics, Data Science, Deep Learning, Internet of Things


Prof. Vijayalakshmi Kakulapati received a Ph.D. in Computer Science & Engineering in Information Retrieval from JNTU Hyderabad. She works as a Professor in the Department of Information Technology, Sreenidhi Institute of Science and Technology, and has around 25 years of industry and teaching experience. She is a member of various professional bodies like IEEE, ACM, CSTA, LMISTE, LMCSI, IACSIT, FIETE, and professional organizations like big data University, etc. She has more than 170+ publications in international journals and conferences, out of which 50+ are in Springer, 4 ACM, 8 IEEE, and  5 in Elsevier. She has 4 granted patents and 5 published patents. She has authored 4 books and 30 book chapters. She took an active role in National Conferences and International Conferences as Session chair. She received more than 14 awards from different organizations. She was awarded a 3 lakhs project from JNTUIII TEQUIP CRC and a 1 lakh fund for the DST workshop. She serves as a review board member for the Journal of Big Data, IAJIT, IEEE Transactions on Computational Social Systems, and many more. She serves as an editorial board member of PLOS One, IJCNS, and more. Currently, she is working with big data analytics, health informatics, Data Science, Artificial Intelligence, Deep learning, machine learning, and the Internet of Things.

Author Articles
Predictive Analytics of Employee Attrition using K-Fold Methodologies

By V. Kakulapati Shaik Subhani

DOI: https://doi.org/10.5815/ijmsc.2023.01.03, Pub. Date: 8 Feb. 2023

Currently, every company is concerned about the retention of their staff. They are nevertheless unable to recognize the genuine reasons for their job resignations due to various circumstances. Each business has its approach to treating employees and ensuring their pleasure. As a result, many employees abruptly terminate their employment for no apparent reason. Machine learning (ML) approaches have grown in popularity among researchers in recent decades. It is capable of proposing answers to a wide range of issues. Then, using machine learning, you may generate predictions about staff attrition. In this research, distinct methods are compared to identify which workers are most likely to leave their organization. It uses two approaches to divide the dataset into train and test data: the 70 percent train, the 30 percent test split, and the K-Fold approaches. Cat Boost, LightGBM Boost, and XGBoost are three methods employed for accuracy comparison. These three approaches are accurately generated by using Gradient Boosting Algorithms.

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Optimization of Fault Learning in Medical Devices

By V. Kakulapati

DOI: https://doi.org/10.5815/ijisa.2022.06.04, Pub. Date: 8 Dec. 2022

A relatively effective training system and advancements in data science demonstrate their evolutionary algorithm power to discover defects and abnormalities in the specified learning process. This work employs a fast and precise fault modelling environment to enhance genetic input implantable devices defect diagnostics. We offer a genetic data technique that incorporates phylogenetic analysis operations and faulty efficiency analysis. This study contributes to fault training in three different ways: 1) it exposes communicative training categories of information formulating adhesion, 2) it introduces a hierarchical system dissemination processing principles to design the fault aggregative, and 3) it indicates forecasting the genetic data sector that corresponds to complicated fault training. The proposed algorithm analyses methods that combine automatically generated fault detection development with massive data testing by non-repetitive fault instances. Analyzing data from validation challenges, infrastructure blowouts, and failure uncertainty make our algorithm more productive in the health sector.

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