FACULTY

Active and accomplished scholars engaging with students

Prof. Jayaraman V.K.

Prof. Jayaraman V.K.

PhD in Chemistry from Savitribai Phule Pune University; M.Tech in Chemical Engineering from Madras University; B. Tech in Chemical Engineering from Madras University.


BIO

Dr. Jayaraman is a Distinguished Professor – Computer Science at FLAME University. He holds a Bachelor’s and Master’s Degree in Chemical Engineering from Madras University and Doctorate in Chemistry from Pune University. His research areas of interest include modelling and simulations in chemical and biochemical engineering, process modelling, control and optimization. For the last ten years he has been working on applications of Machine Learning and Artificial intelligence to different areas. He has several publications in various reputed international journals.


He was associated with the Council of Industrial and Scientific Research (CSIR), India since 1976 where he worked for 33 years and retired as a Deputy Director in 2009. After that he was a CSIR Emeritus Scientist at the Center for Development of Advanced Computing, Pune till January 2013 & thereafter as a consultant.


RESEARCH & PUBLICATIONS

JOURNAL ARTICLES



  • Bhosale, H., Lahorkar, A., Singh, D., Sane, A., & Valadi, J. (2022). Hydropathy and Conformational Similarity-Based Distributed Representation of Protein Sequences for Properties Prediction. SN Computer Science, 3(1), 1-9. https://doi.org/10.1007/s42979-021-00948-3

  • Kadam, K., Peerzada, N., Karbhal, R., Sawant, S., Valadi, J., & Kulkarni-Kale, U. (2021). Antibody Class (es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm. Frontiers in Bioinformatics, 37. https://doi.org/10.3389/fbinf.2021.709951

  • Kaur, A., Chopra, M., Bhushan, M., & Gupta, S. (2021). The Omic Insights on Unfolding Saga of COVID-19. Frontiers in Immunology, 12. https://doi.org/10.3389/fimmu.2021.724914

  • Bhosale, H., Ramakrishnan, V., & Jayaraman, V. K. (2021). Support vector machine-based prediction of pore-forming toxins (PFT) using distributed representation of reduced alphabets. Journal of Bioinformatics and Computational Biology, 19(05), 2150028. https://doi.org/10.1142/s0219720021500281

  • Bhosale, H., Ramakrishnan, V., & Jayaraman, V. K. (2021). A Multi-class Machine Learning Framework to Predict Ampicillin-Sulbactam Resistance of Acinetobacter baumannii. International Journal of Automation, Artificial Intelligence and Machine Learning, 2(2), 66-79. (NO DOI )

  • Kaur, A., Chopra, M., Bhushan, M., & Gupta, S…….Jayaraman Valadi…. (2021). The Omic Insights on Unfolding Saga of COVID-19. Frontiers in Immunology, 12. https://doi.org/10.3389/fimmu.2021.724914

  • Saraswathi K, S., Bhosale, H., Ovhal, P., Parlikkad Rajan, N., & Valadi, J. K. (2020). Random Forest and Autoencoder Data-Driven Models for Prediction of Dispersed-Phase Holdup and Drop Size in Rotating Disc Contactors. Industrial & Engineering Chemistry Research, 60(1), 425-435. https://doi.org/10.1021/acs.iecr.0c04149

  • Shah, Y., Sehgal, D., & Valadi, J. K. (2017). Recent trends in antimicrobial peptide prediction using machine learning techniques. Bioinformation, 13(12), 415. https://doi.org/10.6026/97320630013415

  • Mishra, G., Sehgal, D., & Valadi, J. K. (2017). Quantitative structure activity relationship study of the anti-hepatitis peptides employing random forests and extra-trees regressors. Bioinformation, 13(3), 60. https://doi.org/10.6026/97320630013060

  • Kadam, K., Karbhal, R., Jayaraman, V. K., Sawant, S., & Kulkarni-Kale, U. (2017). AllerBase: a comprehensive allergen knowledgebase. Database, 2017. https://doi.org/10.1093/database/bax066

  • Das, P. S., Poddar, R., Sarkar, S., Megha, M., Sundararajan, V. S., Nallapeta, S., ... & Suravajhala, P. (2018). Twelve years of BIOinformatics CLUb for Experimenting Scientists (Bioclues). PeerJ Preprints, 6, e26503v1. https://doi.org/10.7287/peerj.preprints.26503v1

  • Iddamalgoda, L., Das, P. S., Aponso, A., Sundararajan, V. S., Suravajhala, P., & Valadi, J. K. (2016). Data mining and pattern recognition models for identifying inherited diseases: challenges and implications. Frontiers in genetics, 7, 136. https://doi.org/10.3389/fgene.2016.00136

  • Mishra, G., Ananth, V., Shelke, K., Sehgal, D., & Valadi, J. (2016). Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony OptimizationThe Luxembourg database of trichothecene type B F. graminearum and F. culmorum producers. Bioinformation, 12(1), 12. https://doi.org/10.6026/97320630012012

  • Suravajhala, P., Benso, A., & Valadi, J. (2015). Annotation and curation of uncharacterized proteins: systems biology approaches. Frontiers in Genetics, 6, 224. https://doi.org/10.3389/fgene.2015.00224

  • Oak, N., & Jayaraman, V. K. (2014). Identification of ligand binding pockets on nuclear receptors by machine learning methods. Protein and Peptide Letters, 21(8), 808-814. https://doi.org/10.2174/09298665113209990061

  • Srivastava, A., Ghosh, S., Anantharaman, N., & Jayaraman, V. K. (2013). Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests. Journal of Immunological Methods, 387(1-2), 284-292. https://doi.org/10.1016/j.jim.2012.09.013

  • Nair, V., Dutta, M., & Manian, S. S. (2013). Identification of Penicillin-binding proteins employing support vector machines and random forest. Bioinformation, 9(9), 481. https://doi.org/10.6026/97320630009481

  • Yadav, A., & Jayaraman, V. K. (2012). Structure based function prediction of proteins using fragment library frequency vectors. Bioinformation, 8(19), 953. https://doi.org/10.6026/97320630008953

  • Kumari, S. R., Kadam, K., Badwaik, R., & Jayaraman, V. K. (2012). LIPOPREDICT: bacterial lipoprotein prediction server. Bioinformation, 8(8), 394. https://doi.org/10.6026/97320630008394

  • Kadam, K., Prabhakar, P., & K Jayaraman, V. (2012). SVM Prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors. Protein and Peptide Letters, 19(11), 1155-1162. https://doi.org/10.2174/092986612803217042

  • Ramya Kumari, S., Badwaik, R., Sundararajan, V., & K Jayaraman, V. (2012). Defensinpred: defensin and defensin types prediction server. Protein and peptide letters, 19(12), 1318-1323. https://doi.org/10.2174/092986612803521594

  • Khare, H., Ratnaparkhi, V., Chavan, S., & Jayraman, V. (2012). Prediction of protein-mannose binding sites using random forest. Bioinformation, 8(24), 1202. https://doi.org/10.6026/97320630081202

  • Joshi, A. J., Chandran, S., Jayaraman, V. K., & Kulkarni, B. D. (2010). Hybrid support vector machine for imbalanced data in multiclass arrhythmia classification. International Journal of Functional Informatics and Personalised Medicine, 3(1), 29-47. https://doi.org/10.1504/ijfipm.2010.033244

  • Thomas, S., Karnik, S., Barai, R. S., Jayaraman, V. K., & Idicula-Thomas, S. (2010). CAMP: a useful resource for research on antimicrobial peptides. Nucleic acids research, 38(suppl_1), D774-D780. CAMP: a useful resource for research on antimicrobial peptides

  • Patil, D., Raj, R., Shingade, P., Kulkarni, B., & Jayaraman, V. K. (2009). Feature selection and classification employing hybrid ant colony optimization/random forest methodology. Combinatorial chemistry & high throughput screening, 12(5), 507-513. https://doi.org/10.2174/138620709788488993

  • Karnik, S., Prasad, A., Diwevedi, A., Sundararajan, V., & Jayaraman, V. K. (2009, December). Identification of Defensins employing recurrence quantification analysis and random Forest classifiers. In International Conference on Pattern Recognition and Machine Intelligence (pp. 152-157). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_25

  • Kulkarni, A. J., Jayaraman, V. K., & Kulkarni, B. D. (2009). Review on lazy learning regressors and their applications in QSAR. Combinatorial Chemistry & High Throughput Screening, 12(4), 440-450. https://doi.org/10.2174/138620709788167908

  • Gandhi, A. B., Gupta, P. P., Joshi, J. B., Jayaraman, V. K., & Kulkarni, B. D. (2009). Development of unified correlations for volumetric mass-transfer coefficient and effective interfacial area in bubble column reactors for various gas− liquid systems using support vector regression. Industrial & engineering chemistry research, 48

  • Gupta, P. P., Merchant, S. S., Bhat, A. U., Gandhi, A. B., Bhagwat, S. S., Joshi, J. B., ... & Kulkarni, B. D. (2009). Development of correlations for overall gas hold-up, volumetric mass transfer coefficient, and effective interfacial area in bubble column reactors using hybrid genetic algorithm-support vector regression technique: viscous Newtonian and non-Newtonian liquids. Industrial & engineering chemistry research, 48(21), 9631-9654. https://doi.org/10.1021/ie801834w

  • Rajappan, R., Shingade, P. D., Natarajan, R., & Jayaraman, V. K. (2009). Quantitative Structure− Property Relationship (QSPR) Prediction of Liquid Viscosities of Pure Organic Compounds Employing Random Forest Regression. Industrial & engineering chemistry research, 48(21), 9708-9712. https://doi.org/10.1021/ie8018406

  • Meshram, M., Kulkarni, A., Jayaraman, V. K., Kulkarni, B. D., & Lele, S. S. (2008). Optimal xylanase production using Penicilium janthinellum NCIM 1169: A model based approach. Biochemical engineering journal, 40(2), 348-356. https://doi.org/10.1016/j.bej.2008.01.003

  • Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2008). Multicanonical jump walk annealing assisted by tabu for dynamic optimization of chemical engineering processes. European Journal of Operational Research, 185(3), 1213-1229.  https://doi.org/10.1016/j.ejor.2006.06.049

  • Gandhi, A. B., Joshi, J. B., Kulkarni, A. A., Jayaraman, V. K., & Kulkarni, B. D. (2008). SVR-based prediction of point gas hold-up for bubble column reactor through recurrence quantification analysis of LDA time-series. International journal of multiphase flow, 34(12), 1099-1107. https://doi.org/10.1016/j.ijmultiphaseflow.2008.07.001

  • Shelokar, P. S., Siarry, P., Jayaraman, V. K., & Kulkarni, B. D. (2007). Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied mathematics and computation, 188(1), 129-142. https://doi.org/10.1016/j.amc.2006.09.098

  • Mundra, P., Kumar, M., Kumar, K. K., Jayaraman, V. K., & Kulkarni, B. D. (2007). Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM. Pattern Recognition Letters, 28(13), 1610-1615. https://doi.org/10.1016/j.patrec.2007.04.001

  • Mitra, J., Mundra, P., Kulkarni, B. D., & Jayaraman, V. K. (2007). Using recurrence quantification analysis descriptors for protein sequence classification with support vector machines. Journal of Biomolecular Structure and Dynamics, 25(3), 289-297. https://doi.org/10.1080/07391102.2007.10507177

  • Gandhi, A. B., Joshi, J. B., Jayaraman, V. K., & Kulkarni, B. D. (2007). Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas–liquid systems. Chemical Engineering Science, 62(24), 7078-7089. https://doi.org/10.1016/j.ces.2007.07.071

  • Gandhi, A. B., Joshi, J. B., Jayaraman, V. K., & Kulkarni, B. D. (2007). Data-driven dynamic modeling and control of a surface aeration system. Industrial & engineering chemistry research, 46(25), 8607-8613. https://doi.org/10.1021/ie0700765

  • Jade, A. M., Jayaraman, V. K., Kulkarni, B. D., Khopkar, A. R., Ranade, V. V., & Sharma, A. (2006). A novel local singularity distribution based method for flow regime identification: Gas–liquid stirred vessel with Rushton turbine. Chemical engineering science, 61(2), 688-697. https://doi.org/10.1016/j.ces.2005.08.002

  • Idicula-Thomas, S., Kulkarni, A. J., Kulkarni, B. D., Jayaraman, V. K., & Balaji, P. V. (2006). A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli. Bioinformatics, 22(3), 278-284. https://doi.org/10.1093/bioinformatics/bti810

  • Jade, A. M., Jayaraman, V. K., & Kulkarni, B. D. (2006). Improved time series prediction with a new method for selection of model parameters. Journal of physics a: Mathematical and general, 39(30), L483. https://doi.org/10.1088/0305-4470/39/30/l01

  • Kumar, R., Jayaraman, V. K., & Kulkarni, B. D. (2005). An SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples. Pattern Recognition, 38(1), 41-49. https://doi.org/10.1016/j.patcog.2004.06.002

  • Patil, N. S., Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2005). Regression models using pattern search assisted least square support vector machines. Chemical Engineering Research and Design, 83(8), 1030-1037. https://doi.org/10.1205/cherd.03144

  • Gokhale, S. V., Tayal, R. K., Jayaraman, V. K., & Kulkarni, B. D. (2005). Microchannel reactors: applications and use in process development. International Journal of Chemical Reactor Engineering, 3(1). https://doi.org/10.2202/1542-6580.1176

  • Kulkarni, A., Jayaraman, V. K., & Kulkarni, B. D. (2005). Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process. Computers & chemical engineering, 29(10), 2128-2133. https://doi.org/10.1016/j.compchemeng.2005.06.006

  • Kulkarni, O. C., Vigneshwar, R., Jayaraman, V. K., & Kulkarni, B. D. (2005). Identification of coding and non-coding sequences using local Hölder exponent formalism. Bioinformatics, 21(20), 3818-3823. https://doi.org/10.1093/bioinformatics/bti639

  • Kumar, R., Kulkarni, A., Jayaraman, V. K., & Kulkarni, B. D. (2004). Structure-Activity Relationships using Locally Linear Embedding Assisted by Support Vector and Lazy Learning Regressors. Internet Electron. J. Mol. Des, 3(3), 118-133.

  • Kumar, R., Kulkarni, A., Jayaraman, V. K., & Kulkarni, B. D. (2004). Symbolization assisted SVM classifier for noisy data. Pattern Recognition Letters, 25(4), 495-504. https://doi.org/10.1016/j.patrec.2003.12.012

  • Kulkarni, A., Jayaraman, V. K., & Kulkarni, B. D. (2004). Support vector classification with parameter tuning assisted by agent-based technique. Computers & chemical engineering, 28(3), 311-318. https://doi.org/10.1016/s0098-1354(03)00188-1

  • Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2004). An ant colony approach for clustering. Analytica Chimica Acta, 509(2), 187-195. https://doi.org/10.1016/j.aca.2003.12.032

  • Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2004). An ant colony classifier system: application to some process engineering problems. Computers & Chemical Engineering, 28(9), 1577-1584. https://doi.org/10.1016/j.compchemeng.2003.12.004

  • Kumar, R., Jade, A. M., Jayaraman, V. K., & Kulkarni, B. D. (2004). A Hybrid Methodology For On-Line Process Monitoring. International Journal of Chemical Reactor Engineering, 2(1). https://doi.org/10.2202/1542-6580.1119

  • Kulkarni, A. J., Patil, S. V., Jayaraman, V. K., & Kulkarni, B. D. (2003). Memory Based Local Learning: Application to Process Engineering Problems. International Journal of Chemical Reactor Engineering, 1(1). https://doi.org/10.2202/1542-6580.1105

  • Agarwal, M., Jade, A. M., Jayaraman, V. K., & Kulkarni, B. D. (2003). Support vector machines: A useful tool for process engineering applications. Chemical engineering progress, 99(1), 57-62.

  • Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2003). Multiobjective Optimization of Reactor–Regenerator System Using Ant Algorithm. Petroleum science and technology, 21(7-8), 1167-1184. https://doi.org/10.1081/lft-120017882

  • Rajesh, J., Gupta, K., Kusumakar, H. S., Jayaraman, V. K., & Kulkarni, B. D. (2003). A tabu search based approach for solving a class of bilevel programming problems in chemical engineering. Journal of Heuristics, 9(4), 307-319.

  • Jade, A. M., Srikanth, B., Jayaraman, V. K., Kulkarni, B. D., Jog, J. P., & Priya, L. (2003). Feature extraction and denoising using kernel PCA. Chemical Engineering Science, 58(19), 4441-4448. https://doi.org/10.1016/s0009-2509(03)00340-3

  • Kulkarni, A., Jayaraman, V. K., & Kulkarni, B. D. (2003). Control of chaotic dynamical systems using support vector machines. Physics Letters A, 317(5-6), 429-435. https://doi.org/10.1016/j.physleta.2003.09.004

  • Patil, S. V., Jayaraman, V. K., & Kulkarni, B. D. (2002). Optimization of media by evolutionary algorithms for production of polyols. Applied biochemistry and biotechnology, 102(1), 119-128. https://doi.org/10.1385/abab:102-103:1-6:119

  • Summanwar, V. S., Jayaraman, V. K., Kulkarni, B. D., Kusumakar, H. S., Gupta, K., & Rajesh, J. (2002). Solution of constrained optimization problems by multi-objective genetic algorithm. Computers & Chemical Engineering, 26(10), 1481-1492. https://doi.org/10.1016/s0098-1354(02)00125-4

  • Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2002). Ant algorithm for single and multiobjective reliability optimization problems. Quality and Reliability Engineering International, 18(6), 497-514. https://doi.org/10.1002/qre.499

  • Jayaraman, V. K., Kulkarni, B. D., Gupta, K., Rajesh, J., & Kusumaker, H. S. (2001). Dynamic optimization of fed‐batch bioreactors using the ant algorithm. Biotechnology Progress, 17(1), 81-88. https://doi.org/10.1021/bp000133o

  • Datta, B., Jayaraman, V. K., & Kulkarni, B. D. (2001). A comparative study of annealing methods for batch scheduling problems. Chemical Engineering Research and Design, 79(6), 673-683. https://doi.org/10.1205/026387601316971343

  • Rajesh, J., Gupta, K., Kusumakar, H. S., Jayaraman, V. K., & Kulkarni, B. D. (2001). Dynamic optimization of chemical processes using ant colony framework. Computers & Chemistry, 25(6), 583-595. https://doi.org/10.1016/s0097-8485(01)00081-x

  • Jayaraman, V. K., Kulkarni, B. D., & Rao, A. (2001). Theoretical analysis of a packed bed membrane reactor. Chemical Engineering Journal, 84(3), 475-483. https://doi.org/10.1016/s1385-8947(00)00272-2

  • Shelokar, S., Adhikari, S., Vakil, R., Jayaraman, V. K., & Kulkarni, B. D. (2000). Multiobjective ant algorithm for continuous function optimization: combination of strength Pareto fitness assignment and thermodynamic clustering. Foundations of Computing and Decision Sciences, 25(4), 213-230.

  • Rajesh, J., Jayaraman, V. K., & Kulkarni, B. D. (2000). Taboo search algorithm for continuous function optimization. Chemical Engineering Research and Design, 6(78), 845-848. https://doi.org/10.1205/026387600528049

  • Jayaraman, V. K., Kulkarni, B. D., Karale, S., & Shelokar, P. (2000). Ant colony framework for optimal design and scheduling of batch plants. Computers & Chemical Engineering, 24(8), 1901-1912. https://doi.org/10.1016/s0098-1354(00)00592-5

  • Mathur, M., Karale, S. B., Priye, S., Jayaraman, V. K., & Kulkarni, B. D. (2000). Ant colony approach to continuous function optimization. Industrial & engineering chemistry research, 39(10), 3814-3822. https://doi.org/10.1021/ie990700g

  • Konnur, R., Jayaraman, V. K., & Kulkarni, B. D. (2000). Onset of resonance in chaotically driven systems. Applied mathematics letters, 13(8), 69-75. https://doi.org/10.1016/s0893-9659(00)00098-7

  • Rao, A., Jayaraman, V. K., Kulkarni, B. D., Japanwala, S., & Shegaonkar, P. (1999). Improve controller performance with simple fuzzy rules. Hydrocarbon processing, 78(5), 97-100.Improve controller performance with simple fuzzy rules - Publications of the IAS Fellows

  • Sreekumar, P., Jayaraman, V. K., & Kulkarni, B. D. (1998). Monte Carlo and Cellular Automata Modeling of CO Oxidation on a Catalytic Surface Including the Eley− Rideal Step and CO Diffusion. Industrial & engineering chemistry research, 37(6), 2188-2192. https://doi.org/10.1016/0009-2614(94)87085-3

  • Yerrapragada, S. S., Bandyopadhyay, J. K., Jayaraman, V. K., & Kulkarni, B. D. (1997). Analysis of bifurcation patterns in reaction-diffusion systems: Effect of external noise on the Brusselator model. Physical Review E, 55(5), 5248. https://doi.org/10.1103/physreve.55.5248

  • Bandyopadhyay, J. K., Tambe, S. S., Jayaraman, V. K., Deshpande, P. B., & Kulkarni, B. D. (1997). On control of nonlinear system dynamics at unstable steady state. Chemical Engineering Journal, 67(2), 103-114. https://doi.org/10.1016/s1385-8947(97)00024-7

  • Jayaraman, V. K., & Kulkarni, B. D. (1997). An efficient algorithm for solving hollow-fiber bioreactor design equations. The Chemical Engineering Journal and The Biochemical Engineering Journal, 65(1), 77-80. https://doi.org/10.1016/s1385-8947(96)03155-5

  • Jayaraman, V. K. (1995). Shape normalization of biporous pellets. The Chemical Engineering Journal and The Biochemical Engineering Journal, 59(2), 177-180.  https://doi.org/10.1016/0923-0467(94)02926-1

  • Tambe, S. S., Jayaraman, V. K., & Kulkarni, B. D. (1994). Cellular automata modelling of a surface catalytic reaction with Eley-Rideal step: the case of CO oxidation. Chemical physics letters, 225(4-6), 303-308. https://doi.org/10.1016/0009-2614(94)87085-3

  • Jayaraman, V. K. (1994). Solution of hollow fibre bioreactor design equations: the case of power-law fluids. The Chemical Engineering Journal and the Biochemical Engineering Journal, 55(3), B73-B75. https://doi.org/10.1016/0923-0467(94)06058-4

  • Jayaraman, V. K. (1994). Effect of intraparticle convection on the effectiveness of a biporous pellet. Industrial & engineering chemistry research, 33(2), 273-276. https://doi.org/10.1021/ie00026a015

  • Jayaraman, V. K. (1993). Solution of hollow fibre bioreactor design equations for zero-order limit of Michaelis-Menten kinetics. The Chemical Engineering Journal, 51(3), B63-B66. https://doi.org/10.1016/0300-9467(93)80032-j

  • Jayaraman, V. K. (1993). An algorithm for solving bidisperse catalyst pellet problems. Computers & chemical engineering, 17(7), 639-642.  https://doi.org/10.1016/0098-1354(93)80051-n

  • Jayaraman, V. K. (1992). The solution of hollow fiber bioreactor design equations. Biotechnology progress, 8(5), 462-464. https://doi.org/10.1021/bp00017a014

  • Jayaraman, V. K. (1992). Effectiveness of biporous catalysts for zero-order reactions. Journal of Catalysis, 133(1), 260-262.  https://doi.org/10.1016/0021-9517(92)90202-s

  • Jayaraman, V. K. (1991). A simple method of solution for a class of bioreaction-diffusion problems. Biotechnology letters, 13(6), 455-460. https://doi.org/10.1007/bf01031001

  • SRINIVAS, Y., JAYARAMAN, V., & KULKARNI, B. (1989). ANALYSIS OF NONISOTHERMAL NONADIABATIC TUBULAR REACTORS IN THE PRESENCE OF EXTERNAL DISTURBANCES. HUNGARIAN JOURNAL OF INDUSTRIAL CHEMISTRY, 17(3), 383-396.  1989_17_HJIC-370-382_ocr.pdf (uni-pannon.hu)

  • Jayaraman, V. K., Hlavacek, V., & Puszynski, J. (1987). An expanding core model for a heterogeneous, noncatalytic, gas-solid reaction. Industrial & engineering chemistry research, 26(5), 1048-1050.https://doi.org/10.1021/ie00065a035

  • Dhupe, A. P., Jayaraman, V. K., Gokarn, A. N., & Doraiswamy, L. K. (1987). A experimental study of the effect of inerts on gas-solid reactions. Chemical engineering science, 42(10), 2285-2290.https://doi.org/10.1016/0009-2509(87)80101-x

  • Puszynski, J., Jayaraman, V. K., & Hlavacek, V. (1985). A Stefan problem for exothermic non-catalytic reactions. International journal of heat and mass transfer, 28(6), 1237-1239.https://doi.org/10.1016/0017-9310(85)90133-4

  • Kumar, V. R., Jayaraman, V. K., Kulkarni, B. D., & Doraiswamy, L. K. (1983). Dynamic behaviour of coupled CSTRs operating under different conditions. Chemical Engineering Science, 38(5), 673-686.https://doi.org/10.1016/0009-2509(83)80180-8

  • Jayaraman, V. K., Kulkarni, B. D., & Doraiswamy,  L. K. (1983). Simple method for solution of a class of reaction‐diffusion problems. AIChE Journal, 29(3), 521-523.

  • Jayaraman, V. K., & Doraiswamy, L. K. (1983). Some aspects of diffusional interference in catalytic reactions. Current Science, 280-290.SOME ASPECTS OF DIFFUSIONAL INTERFERENCE IN CATALYTIC REACTIONS on JSTOR

  • Jayaraman, V. K., Kulkarni, B. D . (1982).Anote on Dynamic behaviour of CSTR.  Chemical Engineering Science, 37(3), 475-4766.https://doi.org/10.1016/0009-2509(82)80101-2

  • Jayaraman, V. K., Kulkarni, B. D., & Doraiswamy, L. K. (1981). An Initial Value Approach to the Counter-Current Backmixing Model of the Fluid Bed. https://doi.org/10 .1016/0300-9467(81)80010-x

  • Kulkarni, B. D., Jayaraman, V. K., & Doraiswamy, L. K. (1981). Effectiveness factors in bidispersed catalysts: the general nth order case. Chemical Engineering Science, 36(5), 943-945. https://doi.org/10.1016/0009-2509(81)85050-6

  • Irani, R. K., Jayaraman, V. K., Kulkarni, B. D., & Doraiswamy, L. K. (1981). Optimal production of intermediate for zero—first and first—first order reaction sequences in fluidised bed reactors. Chemical Engineering Science, 36(1), 29-36. https://doi.org/10.1016/0009-2509(81)85050-6

  • Jayaraman, V. K., Ravikumar, V., & Kulkarni, B. D. (1981). Isothermal multiplicity on catalytic surfaces: Application to CO oxidation. Chemical Engineering Science, 36(10), 1731-1734.  https://doi.org/10.1016/0009-2509(81)80020-6

  • Jayaraman, V. K., & Kulkarni, B. D. (1981). An effectiveness factor for surface diffusion with product inhibition. The Chemical Engineering Journal, 21(3), 261-264.   https://doi.org/10.1016/0009-2509(81)80020-6


BOOKS



  • Valadi, J., & Siarry, P. (Eds.). (2014). Applications of metaheuristics in process engineering. https://doi.org/10.1007/978-3-319-06508-3


BOOK CHAPTERS



  • Pandya, M., Valadi, J. (2022). Random Forest Classification and Regression Models for Literacy Data. In: Uddin, M.S., Jamwal, P.K., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-0332-8_18

  • Modak, S., Lahorkar, A., & Valadi, J. (2022). Recent Advances in Applications of Support Vector Machines in Fungal Biology. Laboratory Protocols in Fungal Biology, 117-136. https://doi.org/10.1007/978-3-030-83749-5_6

  • Joshi, T., Lahorkar, A., Tikhe, G., Bhosale, H., Sane, A., & Valadi, J. K. (2021). An improved ant colony optimization with correlation and Gini importance for feature selection. In Communication and Intelligent Systems (pp. 629-641). Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_50

  • Ovhal, P., & Valadi, J. K. . Black Hole—White Hole algorithm for dynamic optimization of chemically reacting systems. In Congress on Intelligent Systems (pp. 535-546). Springer, Singapore. https://doi.org/10.1007/978-981-33-6984-9_43

  • Tikhe, G., Joshi, T., Lahorkar, A., Sane, A., & Valadi, J. (2021). Feature selection using equilibrium optimizer. In Data Engineering and Intelligent Computing (pp. 307-315). Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_29

  • Modak, S., & Valadi, J. (2021). Protein Function Prediction: Human Sequences and Structures. Bioinformatics and Human Genomics Research, 31-56. https://doi.org/10.1201/9781003005926-3

  • Prasad, A., Bhargava, H., Gupta, A., Shukla, N., Rajagopal, S., Gupta, S., ... & Suravajhala, P. (2021). Next Generation Sequencing. In Advances in Bioinformatics (pp. 277-302). Springer, Singapore. https://doi.org/10.1007/978-981-33-6191-1_14

  • Bhargava, H., Sharma, A., & Valadi, J. K. (2021). Machine Learning for Bioinformatics. In Your Passport to a Career in Bioinformatics (pp. 103-108). Springer, Singapore. https://doi.org/10.1007/978-981-15-9544-8_11

  • Agarwal, T., Suravajhala, R., Bhushan, M., Goswami, P., Iddamalgoda, L., Malik, B., ... & Suravajhala, P. (2020). Recent Advances in Gene and Genome Assembly: Challenges and Implications. Advances in Synthetic Biology, 199-220.Singh, V. (Ed.). (2020). Advances in Synthetic Biology. Springer Nature. https://doi.org/10.1007/978-981-15-0081-7_12

  • Modak, S., Mehta, S., Sehgal, D., Valadi, J. (2019). Application of Support Vector Machines in Viral Biology. In: , et al. Global Virology III: Virology in the 21st Century. Springer, Cham. https://doi.org/10.1007/978-3-030-29022-1_12

  • Modak, S., Sehgal, D., & Valadi, J. (2019). Applications of Artificial Intelligence and Machine Learning in Viral Biology. In Global Virology III: Virology in the 21st Century (pp. 1-39). Springer, Cham. https://doi.org/10.1007/978-3-030-29022-1_1

  • Shelokar, P., Kulkarni, A., Jayaraman, V. K., & Siarry, P. (2014). Metaheuristics in process engineering: A historical perspective. In Applications of Metaheuristics in Process Engineering (pp. 1-38). Springer, Cham.

  • Modak, S., Sharma, S., Prabhakar, P., Yadav, A., & Jayaraman, V. K. (2013). Application of support vector machines in fungal genome and proteome annotation. In Laboratory Protocols in Fungal Biology (pp. 565-577). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2356-0_56

  • Jayaraman, V. K., Shelokar, P. S., Shingade, P., Pote, V., Baskar, R., & Kulkarni, B. D. (2010). Ant colony optimization: details of algorithms suitable for process engineering. In Stochastic Global Optimization: Techniques and Applications in Chemical Engineering (With CD-ROM) (pp. 237-269).  https://doi.org/10.1142/9789814299213_0007

  • Kumar, P., Kulkarni, B. D., & Jayaraman, V. K. (2010). Granular Support Vector Machine Based Method for Prediction of Solubility of Proteins on Over Expression in Escherichia Coli and Breast Cancer Classification. In Machine Interpretation Of Patterns: Image Analysis and Data Mining (pp. 289-305). https://doi.org/10.1142/9789814299190_0015

  • Gupta, A., Samanta, A. N., Kulkarni, B. D., & Jayaraman, V. K. (2007, December). Fault diagnosis using dynamic time warping. In International Conference on Pattern Recognition and Machine Intelligence (pp. 57-66). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_8

  • Gupta, A., Jayaraman, V. K., & Kulkarni, B. D. (2007). Feature selection for cancer classification using ant colony optimization and support vector machines. In Analysis of Biological Data: A Soft Computing Approach (pp. 259-280). https://doi.org/10.1142/9789812708892_0011

  • Kumar, P., Gupta, A., Jayaraman, V. K., & Kulkarni, B. (2007). Aligning time series with genetically tuned dynamic time warping algorithm. In Advances in Metaheuristics for Hard Optimization (pp. 251-261). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72960-0_12

  • Joshi, A., Chandran, S., Phadke, S., Jayaraman, V. K., & Kulkarni, B. D. (2005, December). Arrhythmia classification using local hölder exponents and support vector machine. In International Conference on Pattern Recognition and Machine Intelligence (pp. 242-247). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_33

  • Summanwar, V. S., Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2002). Ant colony framework for process optimization: Unconstrained and constrained problems with single and multiple objectives. http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14666279

  • Jayaraman, V. K., Kulkarni, B. D., & Doraiswamy, L. K. (1981). An Initial Value Approach to the Counter-Current Backmixing Model of the Fluid Bed. https://doi.org/10.1021/bk-1981-0168.ch002


 


CONFERENCES



  • Mayur Pandya, Renu Dhadwal and Jayaraman Valadi, Support Vector Machines and Random Forest Models for Identification of Stability in Extrusion Film Casting Process., Paper presented at the 6th International Conference on Data Management, Analytics and Innovation (ICDMAI 2022), Jan.14-15, 2022. Virtual. (Second Prize for Presentation)

  • Hrushikesh Bhosale, Prasad Ovhal, Aamod Sane and Jayaraman Valadi, Improving Black Hole Algorithm Performance by Coupling with Genetic Algorithm for Feature Selection. Paper presented at the Second Congress on Intelligent Systems (CIS 2021), Sept.4-5, 2021. Virtual.

  • Joshi T, Lahorkar A, Tikhe G, Bhosale H, Sane A, Valadi JK. An improved ant colony optimization with correlation and Gini importance for feature selection. Paper presented at the international conference on intelligent systems(ICCIS-2020)Dec.26-27,2020. Virtual.

  • Tikhe G, Joshi T, Lahorkar A, Sane A, Valadi J. Feature selection using equilibrium optimizer. InData Engineering and Intelligent Computing 2021 (pp. 307-315).  Paper presented at the fourth international conference on intelligent computing and communication (ICICC2020), 18-20 September 2020. Virtual

  • Ovhal PT, Valadi JK, Sane A. Twin and multiple Black Holes algorithm for feature selection. In2020 IEEE-HYDCON 2020 Sep 11 (pp. 1-6). IEEE. Virtual. https://doi.org/10.1109/hydcon48903.2020.9242882

  • S. Surana, D. Gunjal, D. Singh, P. Arora and J. Valadi, "Alphabet reduction and distributed vector representation-based method for classification of antimicrobial peptides," 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020, pp. 2825-2832, doi: 10.1109/BIBM49941.2020.9313565.

  • Rajitha Yasas Wijesekara, Ashwin Lahorkar, Kunal Rathore, and Jayaraman Valadi. 2020. RA2Vec: Distributed Representation of Protein Sequences with Reduced Alphabet Embeddings: RA2Vec: Distributed Representation. In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '20). Association for Computing Machinery, New York, NY, USA, Article 80, 1. https://doi.org/10.1145/3388440.3414925

  • Gurav, A., Nair, V., Gupta, U., Valadi, J. (2015). Glowworm Swarm Based Informative Attribute Selection Using Support Vector Machines for Simultaneous Feature Selection and Classification. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_3

  • Mishra, G., Ananth, V., Shelke, K., Sehgal, D., Valadi, J. (2015). Hybrid ACO Chaos-Assisted Support Vector Machines for Classification of Medical Datasets. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2220-0_8

  • K. Shelke, S. Jayaraman, S. Ghosh and J. Valadi, "Hybrid feature selection and peptide binding affinity prediction using an EDA based algorithm," 2013 IEEE Congress on Evolutionary Computation, 2013, pp. 2384-2389, doi: 10.1109/CEC.2013.6557854.

  • Srivastava, A., Chakrabarti, S., Das, S., Ghosh, S., Jayaraman, V.K. (2013). Hybrid Firefly Based Simultaneous Gene Selection and Cancer Classification Using Support Vector Machines and Random Forests. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_41

  • Sharma, S., Ghosh, S., Anantharaman, N., Jayaraman, V.K. (2012). Simultaneous Informative Gene Extraction and Cancer Classification Using ACO-AntMiner and ACO-Random Forests. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds) Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27443-5_86

  • Ghosh, S., Ramachandran, N., Venkateshwari, C., Jayaraman, V.K. (2012). Hybrid Biogeography Based Simultaneous Feature Selection and Prediction of N-Myristoylation Substrate Proteins Using Support Vector Machines and Random Forest Classifiers. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_43

  • S. Nikumbh, S. Ghosh and V. K. Jayaraman, "Biogeography-based informative gene selection and cancer classification using SVM and Random Forests," 2012 IEEE Congress on Evolutionary Computation, 2012, pp. 1-6, doi: 10.1109/CEC.2012.6256127.

  • Chintalapati, J., Arvind, M., Priyanka, S., Mangala, N., Valadi, J. (2010). Parallel Ant-Miner (PAM) on High Performance Clusters. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_33

  • Karnik, S., Mitra, J., Singh, A., Kulkarni, B.D., Sundarajan, V., Jayaraman, V.K. (2009). Identification of N-Glycosylation Sites with Sequence and Structural Features Employing Random Forests. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_24

  • A. J. Joshi, S. Chandran, V. K. Jayaraman and B. D. Kulkarni, "Hybrid SVM for Multiclass Classification," 2009 IEEE International Conference on Bioinformatics and Biomedicine, 2009, pp. 287-290, doi: 10.1109/BIBM.2009.73.

  • Karnik, S., Prasad, A., Diwevedi, A., Sundararajan, V., Jayaraman, V.K. (2009). Identification of Defensins Employing Recurrence Quantification Analysis and Random Forest Classifiers. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_25

  • A. J. Joshi, S. Chandran, V. K. Jayaraman and B. D. Kulkarni, "Multifractality in arterial pulse," 2008 19th International Conference on Pattern Recognition, 2008, pp. 1-4, doi: 10.1109/ICPR.2008.4761083.

  • A. J. Joshi, S. Chandran, V. K. Jayaraman and B. D. Kulkarni, "Arterial Pulse Rate Variability analysis for diagnoses," 2008 19th International Conference on Pattern Recognition, 2008, pp. 1-4, doi: 10.1109/ICPR.2008.4761757.

  • . Joshi, S. Chandran, V. K. Jayaraman and B. D. Kulkarni, "Arterial Pulse System: Modern Methods For Traditional Indian Medicine," 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 608-611, doi: 10.1109/IEMBS.2007.4352363.

  • K. Thadani, Ashutosh, V. K. Jayaraman and V. Sundararajan, "Evolutionary Selection of Kernels in Support Vector Machines," 2006 International Conference on Advanced Computing and Communications, 2006, pp. 19-24, doi: 10.1109/ADCOM.2006.4289849.