Nitin Choubey
@nmims.edu
Professor & Head - Computer Engineering
Narsee Moonji Institute of Management Studies,Mumbai
- Acedemician with 25+ years of Teaching Experience at Undergraduate, Postgraduate and PhD level includes 18+ as HOD & 02 Years as Associate Dean (Engineering) @ MPSTME, Shirpur.
- Working as HOD-Computer Department & Faculty in-charge for Alumni Relations , Published Three Books, one patent and 67 Papers, in National/International Journals/Conferences.,
- Supervising 03 PhD scholars & Supervised 05 PG, 50+ UG Projects,
- Completed three Research Projects under University Seed Grant Scheme.,
- Earned certificate of specialization with IBM offered online through Coursera :
- “IBM Data Science Professional” by completing (Nine) courses on 24/05/2020.
- “IBM Applied AI” by completing (Six) courses on 13/07/2020.
- “IBM AI Engineering” by completing (Six) courses on 07/09/2020.
EDUCATION
PhD(Computer Science and Engineering)-2014
PhD(Business Management)-2004
RESEARCH INTERESTS
Data Structures, Algorithms, Soft Computing, Machine Learning, Data Science, E-Banking
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sachin Chavan and Nitin Choubey
Springer Science and Business Media LLC
Sachin Chavan and Nitin Choubey
Springer Science and Business Media LLC
Rochan Sharma and Nitin Choubey
AIP Publishing
Sarvesh Bhatnagar and Nitin Choubey
Springer Science and Business Media LLC
Nadeem Akhtar, Nitin S. Choubey, and U. Ragavendran
Springer International Publishing
Rehan Ahmad and Nitin S. Choubey
Springer International Publishing
Nitin S. Choubey and Madan U. Kharat
Elsevier BV
Nitin S. Choubey and Madan U. Kharat
Springer Berlin Heidelberg
N.S. Choubey and M.U. Kharat
IEEE
Grammar Induction (or Grammar Inference or Language Learning) is the process of learning of a grammar from training data of the positive and negative strings of the language. Genetic algorithms are amongst the techniques which provide successful result for the grammar induction. The paper describes a Pushdown Automata (PDA) simulator used to parse the training data with the grammar induced by the Genetic Algorithm process. The grammar is induced by using an extended approach of stochastic mutation scheme based on Adaptive Genetic. The algorithm produces successive generations of individuals, computing their “fitness value” at each step and selecting the best of them when the termination condition is reached. The paper deals with the issues in implementation of the algorithm, chromosome representation and evaluation, selection and replacement strategy, and the genetic operators for crossover and mutation. The model has been implemented, and the results obtained for the set of four languages are presented.
N. S. Choubey and M. U. Kharat
IEEE
Grammar Induction (or Grammar Inference or Language Learning) is the process of learning of a grammar from training data of the positive and negative strings of the language. Genetic algorithms are amongst the techniques which provide successful result for the grammar induction. This paper presents a stochastic Mutation Operator based on an Adapted Genetic Algorithm which works with random mask, with uniform distribution of bits over the chromosome length. The model has been implemented, and the results obtained for the set of four context free languages are presented. The paper also compares the suggested operator with other three mutation operator. The suggested operator has shown fast convergence for the induction of grammar as compared to the other operators used.