Dr Deepak Gupta
Assistant Professor
Department of Computer Science & Engineering
Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India-211004
E-mail: deepakg[at]mnnit[dot]ac[dot]in
Telephone: 9999778726(O) 9485230593(M)
Research Publications
Books:
“Advanced Machine Intelligence and Signal Processing” in Lecture Notes in Electrical Engineering, Springer Singapore, https://doi.org/10.1007/978-981-19-0840-8 with other co-editors Koj Sambyo, Mukesh Prasad and Sonali Agarwal under 3rd International Conference on Machine Intelligence and Signal Processing 2021 at NIT Arunachal Pradesh, India. Hardcover ISBN 978-981-19-0839-2, eBook ISBN 978-981-19-0840-8“Pattern Recognition and Data Analysis with Applications” in Lecture Notes in Electrical Engineering, Springer Singapore, https://doi.org/10.1007/978-981-19-1520-8 with other co-editors Rajat Subhra Goswami, Subhasish Banerjee, M. Tanveer and Ram Bilas Pachori under 3rd International Conference on Machine Intelligence and Signal Processing 2021 at NIT Arunachal Pradesh, India. Hardcover ISBN 978-981-19-1519-2, eBook ISBN 978-981-19-1520-8
Journals:
Umesh Gupta, Deepak Gupta. Least squares structural twin bounded support vector machine on class scatter. Applied Intelligence, (2022). https://doi.org/10.1007/s10489-022-04237-1 (SCI, Impact Factor: 5.019). Upendra Mishra, Deepak Gupta and Barenya B. Hazarika. An Intuitionistic fuzzy random vector functional link classifier. Neural Processing Letters (2022).https://doi.org/10.1007/s11063-022-11043-w. (SCI, Impact Factor: 2.565)Barenya B. Hazarika, Deepak Gupta. Random vector functional link with ε-insensitive Huber loss function for biomedical data classification. Computer Methods and Programs in Biomedicine (2022).https://doi.org/10.1016/j.cmpb.2022.106622. (SCI, Impact Factor: 7.027)Barenya B. Hazarika, Deepak Gupta. 1-Norm random vector functional link networks for classification problems. Complex & Intelligent Systems (2022). https://doi.org/10.1007/s40747-022-00668-y. (SCIE, Impact Factor: 6.7)Umesh Gupta, Deepak Gupta. Bipolar fuzzy based least squares twin bounded support vector machine. Fuzzy Sets and Systems, 449(3) (2022). https://doi.org/10.1016/j.fss.2022.06.009 (SCI, Impact Factor: 4.462). Barenya B. Hazarika, Deepak Gupta. Improved twin bounded large margin distribution machines for binary classification. Journal of Multimedia Tools and Applications (2022). https://doi.org/ 10.1007/s11042-022-13738-7. (SCIE, Impact Factor: 2.577)Barenya B. Hazarika, Deepak Gupta and Narayanan Natarajan. Wavelet kernel least square twin support vector regression for wind speed prediction. Environmental Science and Pollution Research (2022).https://doi.org/10.1007/s11356-022-18655-8. (SCI, Impact Factor: 5.190)Parashjyoti Borah, Deepak Gupta. Affinity and transformed class probability-based fuzzy least squares support vector machines. Fuzzy Sets and Systems (2022). https://doi.org/10.1016/j.fss.2022.03.009. (SCIE, Impact Factor: 4.462)Barenya B. Hazarika, Deepak Gupta. Density Weighted Twin Support Vector Machines for Binary Class Imbalance Learning. Neural Processing Letters 54, 1091–1130 (2021).https://doi.org/10.1007/s11063-021-10671-y. (SCI, Impact Factor: 2.565)Umesh Gupta, Deepak Gupta. Kernel-Target Alignment Based Fuzzy Lagrangian Twin Bounded Support Vector Machine. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 29(5):677-707 (2021). https://doi.org/10.1142/S021848852150029X (SCI, Impact Factor: 1.027) Barenya B. Hazarika, Deepak Gupta and Parashjyoti Borah. An intuitionistic fuzzy kernel ridge regression classifier for binary classification. Applied Soft Computing, 112(4):107816 (2021). https://doi.org/10.1016/j.asoc.2021.107816 (SCI, Impact Factor: 8.263). Bikram Kumar and Deepak Gupta. Universum based Lagrangian twin bounded support vector machine to classify EEG signals. Computer Methods and Programs in Biomedicine 208, 106244 (2021), https://doi.org/10.1016/j.cmpb.2021.106244 (SCI, Impact Factor: 7.027)Deepak Gupta, N. Natrajan and M. Berlin. Short-term wind speed prediction using hybrid machine learning techniques. Environ Sci Pollut Res (2021). https://doi.org/10.1007/s11356-021-15221-6 (SCI, Impact Factor: 5.190)Deepak Gupta and N. Natrajan. Prediction of uniaxial compressive strength of rock samples using density weighted least squares twin support vector regression. Neural Computing and Applications (2021) https://doi.org/10.1007/s00521-021-06204-2 (SCI, Impact Factor: 5.102)Deepak Gupta, Vikash Kumar, Ishan Ayus, M. Vasudevan and N. Natrajan. Short-Term Prediction of Wind Power Density Using Convolutional LSTM Network FME Transactions (2021) 49, 653-663 https://doi:10.5937/fme2103653G (ESCI, Impact Factor: 1.82)Deepak Gupta, Barenya B. Hazarika, M. Berlin, Usha Mary Sharma and Kshitij Mishra. Artificial intelligence for suspended sediment load prediction: a review. Environ Earth Sci 80, 346 (2021). https://doi.org/10.1007/s12665-021-09625-3 (SCI, Impact Factor: 3.119)Deepak Gupta, Parashjyoti Borah, Usha Mary Sharma and Mukesh Prasad. Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis. Neural Comput & Applic, 34,11335–11345 (2021). https://doi.org/10.1007/s00521-021-05866-2. (SCI, Impact Factor: 5.102) Umesh Gupta and Deepak Gupta. Least squares large margin distribution machine for regression. Applied Intelligence, 51, 7058–7093 (2021). https://doi.org/10.1007/s10489-020-02166-5 (SCI, Impact Factor: 5.019)Deepak Gupta and Umesh Gupta. On robust asymmetric Lagrangian ν-twin support vector regression using pinball loss function. Applied Soft Computing, 102(2021), 107099, https://doi.org/10.1016/j.asoc.2021.107099 (SCI, Impact Factor: 8.263)Umesh Gupta and Deepak Gupta, Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification. International Journal of Machine Learning and Cybernetics, 12, 1311–1342, (2021). https://doi.org/10.1007/s13042-020-01235-y, (SCI, Impact Factor: 4.377)Deepak Gupta, Ambika Choudhury, Umesh Gupta, Priyanka Singh and Mukesh Prasad. Computational approach to clinical diagnosis of diabetes disease: a comparative study, Journal of Multimedia Tools and Applications, 80, 30091–30116, (2021). https://doi.org/10.1007/s11042-020-10242-8 (SCI, Impact Factor: 2.577)Umesh Gupta and Deepak Gupta. On Regularization based Twin Support Vector Regression with Huber Loss. Neural Processing Letters, 53, 459–515 (2021). https://doi.org/10.1007/s11063-020-10380-y (SCI, Impact Factor: 2.565)Parashjyoti Borah and Deepak Gupta. Robust Twin Bounded Support Vector Machines for Outliers and Imbalanced Data. Applied Intelligence, 51, 5314–5343 (2021). https://doi.org/10.1007/s10489-020-01847-5 (SCI, Impact Factor: 5.019)Deepak Gupta, Bharat Richhariya. Efficient implicit Lagrangian twin parametric insensitive support vector regression via unconstrained minimization problems. Annals of Mathematics and Artificial Intelligence, Springer, 89 (3), 301-332 (2021). https://doi.org/10.1007/s10472-020-09708-0 (SCI, Impact Factor: 1.019)Barenya B. Hazarika, Deepak Gupta, and M. Berlin. A coiflet LDMR and coiflet OB‐ELM for river suspended sediment load prediction. International Journal of Environmental Science & Technology, Springer 18(9), 2675-2692 (2021). https://doi.org/10.1007/s13762-020-02967-8 (SCI, Impact Factor: 3.519) Barenya B. Hazarika, Deepak Gupta. Density-weighted support vector machines for binary class imbalance learning. Neural Comput & Applic, 33, 4243–4261 (2021). https://doi.org/10.1007/s00521-020-05240-8. (SCI, Impact Factor: 5.102)Barenya B. Hazarika, Deepak Gupta. Modelling and Forecasting of COVID-19 Spread using Wavelet-coupled Random Vector Functional Link Networks. Applied Soft Computing, 96(2020), 106626. https://doi.org/10.1007/s12665-020-08949-w (SCI, Impact Factor: 8.263)Debjyoti Das Adhikari, Deepak Gupta. "Applying over 100 classifiers for churn prediction in telecom companies" Journal of Multimedia Tools and Applications 80, 35123–35144 (2020). https://doi.org/10.1007/s11042-020-09658-z (SCIE, Impact Factor: 2.577)Barenya B. Hazarika, Deepak Gupta, and M. Berlin. Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction. Environ Earth Sci 79, 234 (2020). https://doi.org/10.1007/s12665-020-08949-w (SCI, Impact Factor: 3.119)Deepak Gupta, Barenya B. Hazarika, and M. Berlin. Robust regularized extreme learning machine with asymmetric Huber loss function. Neural Computing & Applications, Springer, 32, 12971–12998, (2020). https://doi.org/10.1007/s00521-020-04741-w (SCI, Impact Factor: 5.606)Parashjyoti Borah and Deepak Gupta. Unconstrained convex minimization based implicit Lagrangian twin extreme learning machine for classification (ULTELMC). Applied Intelligence, Springer, 50, 1327-1344 (2020). DOI:10.1007/s10489-019-01596-0 (SCI, Impact Factor: 5.086)Parashjyoti Borah and Deepak Gupta. Functional iterative approaches for solving support vector classification problems based on generalized Huber loss. Neural Computing & Applications, 32, 9245–9265(2020). https://doi.org/10.1007/s00521-019-04436-x (SCI, Impact Factor: 5.102)Deepak Gupta, Kamalini Acharjee and Bharat Richhariya. Lagrangian twin parametric insensitive support vector regression (LTPISVR). Neural Computing & Applications, Springer, 32, 5989–6007 (2020). https://doi.org/10.1007/s00521-019-04084-1 (SCI, Impact Factor: 5.102) Parashjyoti Borah and Deepak Gupta. Unconstrained convex minimization based implicit Lagrangian twin random vector Functional-link networks for binary classification (ULTRVFLC). Applied Soft Computing, Elsevier, 81, 105534 (2019) https://doi.org/10.1016/j.asoc.2019.105534. (SCI, Impact Factor: 8.263)Umesh Gupta and Deepak Gupta. An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function. Applied Intelligence, Springer) 49, 3606–3627, (2019) https://doi.org/10.1007/s10489-019-01465-w. (SCI, Impact Factor: 5.086)Deepak Gupta, Mahardhika Pratama, Zhenyuan Ma, Jun Li and Mukesh Prasad. Financial time series forecasting using twin support vector regression. PLUS ONE, 14(3): e0211402 (2019), https://doi.org/10.1371/journal.pone.0211402Bharat Richhariya and Deepak Gupta. Facial expression recognition using iterative universum twin support vector machine. Applied Soft Computing, Elsevier, 76: 53-67 (2019) https://doi.org/10.1016/j.asoc.2018.11.046. (SCI, Impact Factor: 8.263) Deepak Gupta, Bharat Richhariya and Parashjyoti Borah. A Fuzzy Twin Support Vector Machine based on Information Entropy for Class Imbalance Learning. Neural Computing & Application, Springer, 31, 7153–7164 (2019) https://doi.org/10.1007/s00521-018-3551-9. (SCI, Impact Factor: 5.102) Deepak Gupta and Bharat Richhariya. Entropy based Fuzzy Least Squares Support Vector Machine for Class Imbalance Learning. Applied Intelligence, Springer 48(11): 4212-4231 (2018) https://doi.org/10.1007/s10489-018-1204-4. (SCI, Impact Factor: 5.086) Deepak Gupta. Training primal K-nearest neighbor based weighted twin support vector regression via unconstrained convex minimization. Applied Intelligence, Springer 47(3): 962-991 (2017) https://doi.org/10.1007/s10489-017-0913-4 (SCI, Impact Factor: 5.086) S. Balasundaram, Deepak Gupta, Subhash. A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization. Applied Intelligence, Springer 46(1): 124-134 (2017) https://doi.org/10.1007/s10489-016-0809-8 (TR SCI, Impact Factor: 5.086) S. Balasundaram, Deepak Gupta. Knowledge based extreme learning machines. Neural Computing & Application, Springer 27 (6):1629-1641 (2016) https://doi.org/10.1007/s00521-015-1961-5 (TR SCI, Impact Factor: 5.102) S. Balasundaram, Deepak Gupta. On optimization based extreme learning machine in primal for regression and classification by functional iterative method. International Journal of Machine learning & Cybernetics, Springer 7(5):707-728 (2016) https://doi.org/10.1007/s13042-014-0283-8 (TR SCI, Impact Factor: 4.377) S. Balasundaram, Deepak Gupta & Kapil. Lagrangain support vector regression via unconstrained convex minimization. Neural Networks, Elsevier 51: 67-79 (2014). https://doi.org/10.1016/j.neunet.2013.12.003 (TR SCI, Impact Factor: 9.657) S. Balasundaram, Deepak Gupta & Kapil. 1-norm extreme learning machine for regression and multiclass classification using Newton method. Neurocomputing, Elsevier 128:4-14 (2014). (TR SCI, Impact Factor: 5.779) S. Balasundaram, Deepak Gupta. Training Lagrangain twin support vector regression via unconstrained convex minimization. Knowledge Based Systems, Elsevier 59: 85-96 (2014). (TR SCI, Impact Factor: 8.139) S. Balasundaram, Deepak Gupta. On implicit Lagrangian twin support vector regression by Newton method. The International Journal of Computational Intelligence Systems, Atlantis Press and Taylor & Francis 1: 50-64 (2014) (TR SCI, Impact Factor: 2.153) Barenya B. Hazarika, Deepak Gupta. Affinity based fuzzy kernel ridge regression classifier for binary class imbalance learning. Engineering Applications of Artificial Intelligence (2023). https://doi.org/10.1016/j.engappai.2022.105544. (SCI, Impact Factor: 8)Chittabarni Sarkar, Deepak Gupta, Umesh Gupta and Barenya B. Hazarika. Leaf disease detection using machine learning and deep learning: Review and challenges. Applied Soft Computing,145, 110534 (2023). https://doi.org/10.1016/j.asoc.2023.110534 (SCIE, Impact Factor: 8.7).Barenya B. Hazarika, Deepak Gupta. Mode decomposition based large margin distribution machines for sediment load prediction. Expert Systems with Applications (2023). https://doi.org/10.1016/j.eswa.2023.120844 (SCIE, Impact Factor: 8.5).