Dr. Rukma R
ASSISTANT PROFESSOR
Management
SRM University AP
3
HR analytics, Business Analytics, AI/ML, Blockchain Technology, Innovation adoption, Change management
HR analytics, Business Analytics, HRM/OB
ASSISTANT PROFESSOR
Management
SRM University AP
3
HR analytics, Business Analytics, AI/ML, Blockchain Technology, Innovation adoption, Change management
HR analytics, Business Analytics, HRM/OB
PhD in HR analytics from SRM University AP
MBA (HR & IB) from University of Calicut
BCom (Cooperation) from University of Calicut
Researcher at SRM University AP, from 2021-11-10 to 2025-04-24.
Assistant Professor at Karpagam Academy of Higher Education, from 2021-01-01 to 2021-10-31.
Ramachandran, R., Babu, V., & Murugesan, V. P. (2023). A system to generate a model predicting an employee attrition rate and a method thereof. IN patent office, application number: 202341031320.
Ramachandran, R. & Babu, V. (2023). Revolutionising HR through the deployment of blockchain technology. Blockchain and digital twin enabled IoT networks, pp. 38-58. Taylor & Francis, (Scopus).
Ramachandran, R., Babu, V., & Murugesan, V. P. (2023). Blockchain fragmented clusters for advancing HR saliency: The case of India. India’s technology-led development: Managing transitions to a digital future, pp. 90-120. World Scientific, Singapore. (Scopus)
Ramachandran, R., Babu, V., & Murugesan, V. P. (2023). Human resource analytics revisited: a systematic literature review of its adoption, global acceptance and implementation. Benchmarking: An International Journal, 31(7), 2360-2390. https://doi.org/10.1108/BIJ-04-2022-0272 (Scopus – Q1 ABDC – B) IF = 4.5
Ramachandran, R., Babu, V., & Murugesan, V. P. (2023). The role of blockchain technology in the process of decision-making in human resource management: a review and future research agenda. Business Process Management Journal, 29(1), 116-139. https://doi.org/10.1108/BPMJ-07-2022-0351 (Scopus – Q1 ABDC – B) IF = 4.5
Ramachandran, R., Murugesan, V. P., & Babu, V. (2025). Enhancement of New Random Forest Algorithm to predict the employee attrition rate. International Journal of Enterprise Network Management, (Scopus & ABDC).
The problem of employee attrition in every organization is concerning the employee turnover ratio thereby increasing the cost of investment in human resources. Various factors are reasonable for the rapid attritions at different phased. The purpose of the current study is to predict the employees who are likely to leave the organization. The factors that lead to attrition are identified using Random Forest algorithm. Random Forest algorithm is one of the widely used supervised machine learning techniques for both classificat
UGC NET 2020 (Management)
UGC NET 2019 (Commerce)
