Dr Nishant Jain
@web.mitsgwalior.in
Assistant Professor
Madhav Institute of Technology & Science, Gwalior (M.P.), INDIA
RESEARCH, TEACHING, or OTHER INTERESTS
Multidisciplinary, Multidisciplinary
6
Scopus Publications
Scopus Publications
- Early Autism Spectrum Disorder Prediction Using Fine-Tuned Bernoulli’s Naive Bayes Algorithm
Kanav Gupta, Chirag Paul, and Nishant Jain
Springer Nature Switzerland - SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems
Nishant Jain and Shipra Shukla
Springer Nature Singapore - LRF: A logically randomized forest algorithm for classification and regression problems[Formula presented]
Nishant Jain and Prasanta K. Jana
Elsevier BV - XRRF: An eXplainable Reasonably Randomised Forest algorithm for classification and regression problems
Nishant Jain and Prasanta K. Jana
Elsevier BV - A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning
Nishant Jain, Abhinav Tomar, and Prasanta K. Jana
Springer Science and Business Media LLC - Novel Framework for Performance Prediction of Small and Medium Scale Enterprises: A Machine Learning Approach
Nishant Jain, Abhinav Tomar, and Prasanta K. Jana
IEEE
The small and medium scale enterprises (SMEs) are the prime factor for economic growth and job creation in developing countries. The literature shows that only a small number of SMEs are successful in achieving exceptional performance and sustainable growth. Therefore, it is paramount to determine the socioeconomic factors that hinder their growth. Incorporation of machine learning and statistical methods for solving business problems has gained substantial interest in recent years due to an exponential rise in consumer data. However, processing and interpreting this data to support business decision making is demanding, thereby leaving the scope for advancement. Therefore, in this paper, we design a novel performance framework with four modules, each having different functionality and contemplates machine learning methods. The fundamental objective is to predict the impact of strategic planning on SME's performance so that it can sustain in current competitive markets. For the sake of validating the framework, an experimental case study is conducted for a particular module, i.e., PMM Module. The prediction results for PMM module are compared in terms of RMSE concerning RNN, GBT, and RF methods.