Semisupervised approach for dominant gene selection and classification.

Reshma Rastogi Reshma Khemchandani http://www.sau.int/faculty/faculty-profile.html?staff_id=73 https://scholar.google.com/citations?user=NqIkygEAAAAJ https://zbmath.org/authors/?q=ai:rastogi.reshma https://orcid.org/0000-0002-9322-4251 South Asian University, India

Reshma Rastogi Mamta Bhattarai Lamsal Semisupervised approach for dominant gene selection and classification. 2026 December 75 27 BMC Bioinform. 1 https://doi.org/10.1186/s12859-026-06374-7 db/journals/bmcbi/bmcbi27.html#RastogiL26 streams/journals/bmcbi

Mamta Bhattarai Lamsal Reshma Rastogi Hypergraph-based adaptive manifold learning for gene selection. 2026 December 70 68 Knowl. Inf. Syst. 1 https://doi.org/10.1007/s10115-026-02683-8 db/journals/kais/kais68.html#LamsalR26 streams/journals/kais

Sambhav Jain Reshma Rastogi Unified Twin Support Vector Machines for Large-Scale Pattern Classification. 2026 April 2049-2060 10 IEEE Trans. Emerg. Top. Comput. Intell. 2 https://doi.org/10.1109/TETCI.2025.3641799 db/journals/tetci/tetci10.html#JainR26 streams/journals/tetci

Pooja Saigal Anubhav David Reshma Rastogi Oblique random forests with binary and ternary decision structures and non-parallel hyperplanes classifiers. 131-149 2025 June 20 Int. J. Data Sci. Anal. 1 https://doi.org/10.1007/s41060-023-00472-y db/journals/ijdsa/ijdsa20.html#SaigalDR25 streams/journals/ijdsa

Mamta Bhattarai Lamsal Reshma Rastogi A hypergraph embedded entropy approach to gene selection. 6577-6609 2025 August 67 Knowl. Inf. Syst. 8 https://doi.org/10.1007/s10115-025-02423-4 db/journals/kais/kais67.html#LamsalR25 streams/journals/kais

Shuvo Saha Roy Reshma Rastogi Granular Computing Enhanced Federated Learning Framework on Edge Devices. PReMI (2) 2025 77-84 conf/premi/2025-2 https://doi.org/10.1007/978-3-032-18480-1_8 db/conf/premi/premi2025-2.html#RoyR25 streams/conf/premi Reshma Rastogi Abhijeet Kumar Granular Computing Based Fuzzy Classifier for Tensor Data Classification. PReMI (2) 2025 118-126 conf/premi/2025-2 https://doi.org/10.1007/978-3-032-18480-1_12 db/conf/premi/premi2025-2.html#RastogiK25 streams/conf/premi Ankush Bisht Anirudh Aggarwal Sanjay Kumar Reshma Rastogi Adaptive Weighted Granular Ball Framework for Robust and Efficient Support Vector Regression. PReMI (2) 2025 613-620 conf/premi/2025-2 https://doi.org/10.1007/978-3-032-18480-1_62 db/conf/premi/premi2025-2.html#BishtAKR25 streams/conf/premi

Reshma Rastogi Ankush Bisht Sanjay Kumar Suresh Chandra GBSVR: Granular Ball Support Vector Regression. 2025 March abs/2503.10539 CoRR https://doi.org/10.48550/arXiv.2503.10539 db/journals/corr/corr2503.html#abs-2503-10539 streams/journals/corr

M. Tanveer 0001 Taniya Rajani Reshma Rastogi Yuan-Hai Shao 0001 M. A. Ganaie 0001 Comprehensive review on twin support vector machines. 1223-1268 2024 August 339 Ann. Oper. Res. 3 https://doi.org/10.1007/s10479-022-04575-w db/journals/anor/anor339.html#TanveerRRSG24 streams/journals/anor

Sambhav Jain Reshma Rastogi Parametric non-parallel support vector machines for pattern classification. 1567-1594 2024 April 113 Mach. Learn. 4 https://doi.org/10.1007/s10994-022-06238-0 db/journals/ml/ml113.html#JainR24

Reshma Rastogi Dev Nirwal HyperGraph based Imbalance Multi-label Classification with Label Specific Features. 125-132 2024 COMAD/CODS (December) https://doi.org/10.1145/3703323.3703343 conf/comad/2024d db/conf/comad/comad2024d.html#RastogiN24 Sambhav Jain Reshma Rastogi Establishing Interconnections of Similarity-Based Classifiers for Multi-label Learning with Missing Labels. 50-62 2024 ICPR (2) https://doi.org/10.1007/978-3-031-78166-7_4 conf/icpr/2024-2 db/conf/icpr/icpr2024-2.html#JainR24 streams/conf/icpr Sanjay Kumar Reshma Rastogi Addressing Multi-Label Learning with Missing Labels via Feature Relevance guided Scaled Model Coefficients. 206-222 2024 ICPR (24) https://doi.org/10.1007/978-3-031-78383-8_14 conf/icpr/2024-24 db/conf/icpr/icpr2024-24.html#KumarR24 streams/conf/icpr Reshma Rastogi Dev Nirwal Hypergraph Regularized Semi-supervised Least Squares Twin Support Vector Machine for Multilabel Classification. 223-237 2024 ICPR (24) https://doi.org/10.1007/978-3-031-78383-8_15 conf/icpr/2024-24 db/conf/icpr/icpr2024-24.html#RastogiN24a streams/conf/icpr Reshma Rastogi Dev Nirwal Improved Hypergraph Laplacian Based Semi-supervised Support Vector Machine. 242-255 2024 ICPR (10) https://doi.org/10.1007/978-3-031-78192-6_16 conf/icpr/2024-10 db/conf/icpr/icpr2024-10.html#RastogiN24 streams/conf/icpr Reshma Rastogi Sayanta Chowdhury Binary-Tree Based Mean-Averaging Estimation for Multi-label Classification. 271-285 2024 ICPR (10) https://doi.org/10.1007/978-3-031-78192-6_18 conf/icpr/2024-10 db/conf/icpr/icpr2024-10.html#RastogiC24 streams/conf/icpr Reshma Rastogi Mamta Bhattarai Lamsal Adaptive Graph-Based Manifold Learning for Gene Selection. 453-468 2024 ICPR (1) https://doi.org/10.1007/978-3-031-78107-0_29 conf/icpr/2024-1 db/conf/icpr/icpr2024-1.html#RastogiL24 streams/conf/icpr

Sanjay Kumar Nadira Ahmadi Reshma Rastogi Multi-label learning with missing labels using sparse global structure for label-specific features. 18155-18170 2023 August 53 Appl. Intell. 15 https://doi.org/10.1007/s10489-022-04439-7 db/journals/apin/apin53.html#KumarAR23

Pritam Anand Amisha Bharti Reshma Rastogi Time efficient variants of Twin Extreme Learning Machine. 200169 2023 17 Intell. Syst. Appl. https://doi.org/10.1016/j.iswa.2022.200169 db/journals/iswa/iswa17.html#AnandBR23

Reshma Rastogi Sanjay Kumar Discriminatory Label-specific Weights for Multi-label Learning with Missing Labels. 1397-1431 2023 April 55 Neural Process. Lett. 2 https://doi.org/10.1007/s11063-022-10945-z https://www.wikidata.org/entity/Q114955042 db/journals/npl/npl55.html#RastogiK23

Reshma Rastogi Sambhav Jain Multi-label learning via minimax probability machine. 1-17 2022 145 Int. J. Approx. Reason. https://doi.org/10.1016/j.ijar.2022.02.002 db/journals/ijar/ijar145.html#RastogiJ22

Reshma Rastogi Sayed Mortaza Imbalance multi-label data learning with label specific features. 395-408 2022 513 Neurocomputing https://doi.org/10.1016/j.neucom.2022.09.085 db/journals/ijon/ijon513.html#RastogiM22

Sanjay Kumar Reshma Rastogi Low rank label subspace transformation for multi-label learning with missing labels. 53-72 2022 596 Inf. Sci. https://doi.org/10.1016/j.ins.2022.03.015 db/journals/isci/isci596.html#KumarR22

Sanjay Kumar Reshma Rastogi Auxiliary Label Embedding for Multi-label Learning with Missing Labels. 525-537 2022 CVMI https://doi.org/10.1007/978-981-19-7867-8_42 conf/cvmi/2022 db/conf/cvmi/cvmi2022.html#KumarR22

Mohammad Tanveer 0001 Sweta Sharma Reshma Rastogi Pritam Anand Sparse support vector machine with pinball loss. 2021 32 Trans. Emerg. Telecommun. Technol. 2 https://doi.org/10.1002/ett.3820 https://www.wikidata.org/entity/Q126299942 db/journals/ett/ett32.html#TanveerSRA21

Reshma Rastogi Sayed Mortaza Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning. 107336 2021 229 Knowl. Based Syst. https://doi.org/10.1016/j.knosys.2021.107336 db/journals/kbs/kbs229.html#RastogiM21

Reshma Rastogi Sweta Sharma Ternary tree-based structural twin support tensor machine for clustering. 61-74 2021 24 Pattern Anal. Appl. 1 https://doi.org/10.1007/s10044-020-00902-8 db/journals/paa/paa24.html#RastogiS21

Sweta Sharma Reshma Rastogi Suresh Chandra 0001 Large-Scale Twin Parametric Support Vector Machine Using Pinball Loss Function. 987-1003 2021 51 IEEE Trans. Syst. Man Cybern. Syst. 2 https://doi.org/10.1109/TSMC.2019.2896642 db/journals/tsmc/tsmc51.html#SharmaRC21

Reshma Rastogi Sweta Sharma Suresh Chandra 0001 Stochastic Quasi-Newton Minimum Complexity Machine for Pattern Classification. 520-528 2021 PReMI https://doi.org/10.1007/978-3-031-12700-7_53 conf/premi/2021 db/conf/premi/premi2021.html#RastogiSC21 streams/conf/premi

Mohammad Tanveer 0001 Taniya Rajani Reshma Rastogi Yuan-Hai Shao 0001 Comprehensive Review On Twin Support Vector Machines. 2021 abs/2105.00336 CoRR https://arxiv.org/abs/2105.00336 db/journals/corr/corr2105.html#abs-2105-00336

Pritam Anand Reshma Rastogi Suresh Chandra 0001 Improvement over Pinball Loss Support Vector Machine. 2021 abs/2106.01109 CoRR https://arxiv.org/abs/2106.01109 db/journals/corr/corr2106.html#abs-2106-01109

Reshma Rastogi Aman Pal Efficient Learning of Pinball TWSVM using Privileged Information and its applications. 2021 abs/2107.06744 CoRR https://arxiv.org/abs/2107.06744 db/journals/corr/corr2107.html#abs-2107-06744

Pritam Anand Reshma Rastogi Suresh Chandra 0001 A class of new Support Vector Regression models. 106446 2020 94 Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2020.106446 db/journals/asc/asc94.html#AnandRC20

Pritam Anand Reshma Rastogi Suresh Chandra 0001 A new asymmetric ϵ-insensitive pinball loss function based support vector quantile regression model. 106473 2020 94 Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2020.106473 db/journals/asc/asc94.html#AnandRC20a

Reshma Rastogi Pritam Anand Suresh Chandra 0001 Large-margin Distribution Machine-based regression. 3633-3648 2020 32 Neural Comput. Appl. 8 https://doi.org/10.1007/s00521-018-3921-3 https://www.wikidata.org/entity/Q128700469 db/journals/nca/nca32.html#RastogiAC20

Pooja Saigal Reshma Rastogi Suresh Chandra 0001 Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification. 1555-1582 2020 52 Neural Process. Lett. 2 https://doi.org/10.1007/s11063-020-10323-7 db/journals/npl/npl52.html#SaigalRC20

Pooja Saigal Reshma Khemchandani Nonparallel Hyperplane Classifiers for Multi-category Classification. 2020 abs/2004.07512 CoRR https://arxiv.org/abs/2004.07512 db/journals/corr/corr2004.html#abs-2004-07512

Reshma Rastogi Sweta Sharma Fast Laplacian twin support vector machine with active learning for pattern classification. 424-439 2019 74 Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2018.10.042 https://www.wikidata.org/entity/Q129015649 db/journals/asc/asc74.html#RastogiS19

Pooja Saigal Suresh Chandra 0001 Reshma Rastogi Multi-category ternion support vector machine. 229-242 2019 85 Eng. Appl. Artif. Intell. https://doi.org/10.1016/j.engappai.2019.06.014 https://www.wikidata.org/entity/Q127585983 db/journals/eaai/eaai85.html#SaigalCR19

Reshma Rastogi Aman Pal Fuzzy semi-supervised weighted linear loss twin support vector clustering. 132-148 2019 165 Knowl. Based Syst. https://doi.org/10.1016/j.knosys.2018.11.027 https://www.wikidata.org/entity/Q128850955 db/journals/kbs/kbs165.html#RastogiP19

Reshma Rastogi Simran Popli Nima Dorji Moktan Sweta Sharma A Unified Multi-label Relationship Learning. 686-691 2019 ICCSE https://doi.org/10.1109/ICCSE.2019.8845526 conf/iccse2/2019 db/conf/iccse2/iccse2019.html#RastogiPMS19 Reshma Rastogi Ritesh Gangnani Semi-supervised Multi-category Classification with Generative Adversarial Networks. 286-294 2019 PReMI (1) https://doi.org/10.1007/978-3-030-34869-4_32 conf/premi/2019-1 db/conf/premi/premi2019-1.html#RastogiG19

Pritam Anand Reshma Rastogi Suresh Chandra 0001 Support Vector Regression via a Combined Reward Cum Penalty Loss Function. 2019 abs/1904.12331 CoRR http://arxiv.org/abs/1904.12331 db/journals/corr/corr1904.html#abs-1904-12331

Pritam Anand Reshma Rastogi Suresh Chandra 0001 A new asymmetric ε-insensitive pinball loss function based support vector quantile regression model. 2019 abs/1908.06923 CoRR http://arxiv.org/abs/1908.06923 db/journals/corr/corr1908.html#abs-1908-06923

Pritam Anand Reshma Rastogi Suresh Chandra 0001 A ν-support vector quantile regression model with automatic accuracy control. 2019 abs/1910.09168 CoRR http://arxiv.org/abs/1910.09168 db/journals/corr/corr1910.html#abs-1910-09168

Reshma Khemchandani Pooja Saigal Suresh Chandra 0001 Angle-based twin support vector machine. 387-417 2018 269 Ann. Oper. Res. 1-2 https://doi.org/10.1007/s10479-017-2604-2 db/journals/anor/anor269.html#KhemchandaniSC18

Reshma Rastogi Aman Pal Suresh Chandra 0001 Generalized Pinball Loss SVMs. 151-165 2018 322 Neurocomputing https://doi.org/10.1016/j.neucom.2018.08.079 https://www.wikidata.org/entity/Q129149673 db/journals/ijon/ijon322.html#RastogiPC18

Reshma Rastogi Pooja Saigal Suresh Chandra 0001 Angle-based twin parametric-margin support vector machine for pattern classification. 64-77 2018 139 Knowl. Based Syst. https://doi.org/10.1016/j.knosys.2017.10.008 db/journals/kbs/kbs139.html#RastogiSC18

Reshma Khemchandani Keshav Goyal Suresh Chandra 0001 Generalized eigenvalue proximal support vector regressor for the simultaneous learning of a function and its derivatives. 2059-2070 2018 9 Int. J. Mach. Learn. Cybern. 12 https://doi.org/10.1007/s13042-017-0687-3 db/journals/mlc/mlc9.html#KhemchandaniGC18

Reshma Khemchandani Aman Pal Suresh Chandra 0001 Fuzzy least squares twin support vector clustering. 553-563 2018 29 Neural Comput. Appl. 2 https://doi.org/10.1007/s00521-016-2468-4 db/journals/nca/nca29.html#KhemchandaniPC18

Reshma Rastogi Sweta Sharma Suresh Chandra 0001 Robust Parametric Twin Support Vector Machine for Pattern Classification. 293-323 2018 47 Neural Process. Lett. 1 https://doi.org/10.1007/s11063-017-9633-3 db/journals/npl/npl47.html#RastogiSC18

Sweta Sharma Reshma Rastogi Stochastic Conjugate Gradient Descent Twin Support Vector Machine for Large Scale Pattern Classification. 590-602 2018 Australasian Conference on Artificial Intelligence https://doi.org/10.1007/978-3-030-03991-2_54 conf/ausai/2018 db/conf/ausai/ausai2018.html#SharmaR18 Ankit Kumar Reshma Rastogi Self-Attention Enhanced Recurrent Neural Networks for Sentence Classification. 905-911 2018 SSCI https://doi.org/10.1109/SSCI.2018.8628865 conf/ssci/2018 db/conf/ssci/ssci2018.html#KumarR18 Aman Pal Reshma Rastogi Learning TWSVM using Privilege Information. 1548-1554 2018 SSCI https://doi.org/10.1109/SSCI.2018.8628645 conf/ssci/2018 db/conf/ssci/ssci2018.html#PalR18 Reshma Rastogi Hamid Safdari Sweta Sharma Exploring Data Reduction Techniques for Time Efficient Support Vector Machine Classifiers. 2053-2059 2018 SSCI https://doi.org/10.1109/SSCI.2018.8628716 conf/ssci/2018 db/conf/ssci/ssci2018.html#RastogiSS18 Pritam Anand Reshma Rastogi Amisha Bharti A Pinball loss based Extreme Learning Machine for pattern classification. 2212-2215 2018 SSCI https://doi.org/10.1109/SSCI.2018.8628905 conf/ssci/2018 db/conf/ssci/ssci2018.html#AnandRB18 Sweta Sharma Reshma Rastogi Insensitive Zone based Pinball Loss Twin Support Vector Machine for Pattern Classification. 2238-2245 2018 SSCI https://doi.org/10.1109/SSCI.2018.8628771 conf/ssci/2018 db/conf/ssci/ssci2018.html#SharmaR18 Sweta Sharma Reshma Rastogi Maximum Margin Minimum Variance Twin Support Vector Machine. 2246-2253 2018 SSCI https://doi.org/10.1109/SSCI.2018.8628859 conf/ssci/2018 db/conf/ssci/ssci2018.html#SharmaR18a Jayadeva Reshma Khemchandani Suresh Chandra 0001 Twin Support Vector Machines - Models, Extensions and Applications 2017 1-206 Studies in Computational Intelligence 659 Springer 978-3-319-46184-7 978-3-319-46186-1 https://doi.org/10.1007/978-3-319-46186-1

Reshma Rastogi Pritam Anand Suresh Chandra 0001 A ν-twin support vector machine based regression with automatic accuracy control. 670-683 2017 46 Appl. Intell. 3 https://doi.org/10.1007/s10489-016-0860-5 db/journals/apin/apin46.html#RastogiAC17

Reshma Rastogi Pooja Saigal Tree-based localized fuzzy twin support vector clustering with square loss function. 96-113 2017 47 Appl. Intell. 1 https://doi.org/10.1007/s10489-016-0886-8 db/journals/apin/apin47.html#RastogiS17

Pooja Saigal Vaibhav Khanna Reshma Rastogi Divide and conquer approach for semi-supervised multi-category classification through localized kernel spectral clustering. 296-306 2017 238 Neurocomputing https://doi.org/10.1016/j.neucom.2017.01.065 db/journals/ijon/ijon238.html#SaigalKR17

Reshma Khemchandani Aman Pal Tree based multi-category Laplacian TWSVM for content based image retrieval. 1197-1210 2017 8 Int. J. Mach. Learn. Cybern. 4 https://doi.org/10.1007/s13042-016-0493-3 db/journals/mlc/mlc8.html#KhemchandaniP17

Reshma Rastogi Sweta Sharma Tree-Based Structural Twin Support Tensor Clustering with Square Loss Function. 28-34 2017 PReMI https://doi.org/10.1007/978-3-319-69900-4_4 conf/premi/2017 db/conf/premi/premi2017.html#RastogiS17

Reshma Khemchandani Avikant Bhardwaj Suresh Chandra 0001 Single asset optimal trading strategies with stochastic dominance constraints. 211-228 2016 243 Ann. Oper. Res. 1-2 https://doi.org/10.1007/s10479-014-1697-0 db/journals/anor/anor243.html#KhemchandaniBC16

Reshma Khemchandani Aman Pal Multi-category laplacian least squares twin support vector machine. 458-474 2016 45 Appl. Intell. 2 https://doi.org/10.1007/s10489-016-0770-6 db/journals/apin/apin45.html#KhemchandaniP16

Reshma Khemchandani Sweta Sharma Robust least squares twin support vector machine for human activity recognition. 33-46 2016 47 Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2016.05.025 db/journals/asc/asc47.html#KhemchandaniS16

Reshma Khemchandani Keshav Goyal Suresh Chandra 0001 TWSVR: Regression via Twin Support Vector Machine. 14-21 2016 74 Neural Networks https://doi.org/10.1016/j.neunet.2015.10.007 https://www.wikidata.org/entity/Q40255873 db/journals/nn/nn74.html#KhemchandaniGC16

Reshma Khemchandani Pooja Saigal Suresh Chandra 0001 Improvements on ν-Twin Support Vector Machine. 97-107 2016 79 Neural Networks https://doi.org/10.1016/j.neunet.2016.03.011 https://www.wikidata.org/entity/Q45951387 db/journals/nn/nn79.html#KhemchandaniSC16

Reshma Khemchandani Aman Pal Weighted Linear Loss Twin Support Vector Clustering. 18:1-18:2 2016 CODS https://doi.org/10.1145/2888451.2888467 conf/cods/2016 db/conf/cods/cods2016.html#KhemchandaniP16 Reshma Khemchandani Sweta Sharma Robust Parametric Twin Support Vector Machine and Its Application in Human Activity Recognition. 193-203 2016 CVIP (1) https://doi.org/10.1007/978-981-10-2104-6_18 conf/cvip/2016-1 db/conf/cvip/cvip2016-1.html#KhemchandaniS16

Reshma Khemchandani Pooja Saigal Color image classification and retrieval through ternary decision structure based multi-category TWSVM. 444-455 2015 165 Neurocomputing https://doi.org/10.1016/j.neucom.2015.03.074 db/journals/ijon/ijon165.html#KhemchandaniS15

Reshma Khemchandani Keshav Goyal Suresh Chandra 0001 Twin Support Vector Machine based Regression. 1-6 2015 ICAPR https://doi.org/10.1109/ICAPR.2015.7050651 conf/icapr/2015 db/conf/icapr/icapr2015.html#KhemchandaniGC15

Reshma Khemchandani Anuj Karpatne Suresh Chandra 0001 Twin support vector regression for the simultaneous learning of a function and its derivatives. 51-63 2013 4 Int. J. Mach. Learn. Cybern. 1 https://doi.org/10.1007/s13042-012-0072-1 db/journals/mlc/mlc4.html#KhemchandaniKC13

Reshma Khemchandani Anuj Karpatne Suresh Chandra 0001 Proximal support tensor machines. 703-712 2013 4 Int. J. Mach. Learn. Cybern. 6 https://doi.org/10.1007/s13042-012-0132-6 db/journals/mlc/mlc4.html#KhemchandaniKC13a

Reshma Khemchandani Nishil Gupta Arpit Chaudhary Suresh Chandra 0001 Optimal execution with weighted impact functions: a quadratic programming approach. 575-592 2013 7 Optim. Lett. 3 https://doi.org/10.1007/s11590-012-0441-4 db/journals/ol/ol7.html#KhemchandaniGCC13

Reshma Khemchandani Anuj Karpatne Suresh Chandra 0001 Generalized eigenvalue proximal support vector regressor. 13136-13142 2011 38 Expert Syst. Appl. 10 https://doi.org/10.1016/j.eswa.2011.04.121 db/journals/eswa/eswa38.html#KhemchandaniKC11

Reshma Khemchandani Jayadeva Suresh Chandra 0001 Learning the optimal kernel for Fisher discriminant analysis via second order cone programming. 692-697 2010 203 Eur. J. Oper. Res. 3 https://doi.org/10.1016/j.ejor.2009.09.020 db/journals/eor/eor203.html#KhemchandaniJC10

M. Arun Kumar Reshma Khemchandani Madan Gopal Suresh Chandra 0001 Knowledge based Least Squares Twin support vector machines. 4606-4618 2010 180 Inf. Sci. 23 https://doi.org/10.1016/j.ins.2010.07.034 db/journals/isci/isci180.html#KumarKGC10

Reshma Khemchandani Jayadeva Suresh Chandra 0001 Knowledge based proximal support vector machines. 914-923 2009 195 Eur. J. Oper. Res. 3 https://doi.org/10.1016/j.ejor.2007.11.023 db/journals/eor/eor195.html#KhemchandaniJC09

Reshma Khemchandani Jayadeva Suresh Chandra 0001 Regularized least squares fuzzy support vector regression for financial time series forecasting. 132-138 2009 36 Expert Syst. Appl. 1 https://doi.org/10.1016/j.eswa.2007.09.035 db/journals/eswa/eswa36.html#KhemchandaniJC09

Reshma Khemchandani Jayadeva Suresh Chandra 0001 Optimal kernel selection in twin support vector machines. 77-88 2009 3 Optim. Lett. 1 https://doi.org/10.1007/s11590-008-0092-7 db/journals/ol/ol3.html#KhemchandaniJC09

Jayadeva Reshma Khemchandani Suresh Chandra 0001 Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives. 3402-3414 2008 178 Inf. Sci. 17 https://doi.org/10.1016/j.ins.2008.04.007 db/journals/isci/isci178.html#JayadevaKC08

Reshma Khemchandani Jayadeva Suresh Chandra 0001 Linear potential proximal support vector machines for pattern classification. 491-500 2008 23 Optim. Methods Softw. 4 https://doi.org/10.1080/10556780802102636 db/journals/oms/oms23.html#KhemchandaniJC08

Jayadeva Reshma Khemchandani Suresh Chandra 0001 Twin Support Vector Machines for Pattern Classification. 905-910 2007 29 IEEE Trans. Pattern Anal. Mach. Intell. 5 https://doi.org/10.1109/TPAMI.2007.1068 http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.1068 https://www.wikidata.org/entity/Q51015227 db/journals/pami/pami29.html#JayadevaKC07

Jayadeva Reshma Khemchandani Suresh Chandra 0001 Fuzzy multi-category proximal support vector classification via generalized eigenvalues. 679-685 2007 11 Soft Comput. 7 https://doi.org/10.1007/s00500-006-0130-2 db/journals/soco/soco11.html#JayadevaKC07

Jayadeva Reshma Khemchandani Suresh Chandra 0001 Regularized Least Squares Fuzzy Support Vector Regression for Time Series Forecasting. 593-598 2006 IJCNN https://doi.org/10.1109/IJCNN.2006.246736 conf/ijcnn/2006 db/conf/ijcnn/ijcnn2006.html#JayadevaKC06 Jayadeva Reshma Khemchandani Suresh Chandra 0001 Regularized Least Squares Twin SVR for the Simultaneous Learning of a Function and its Derivative. 1192-1197 2006 IJCNN https://doi.org/10.1109/IJCNN.2006.246826 conf/ijcnn/2006 db/conf/ijcnn/ijcnn2006.html#JayadevaKC06a

Jayadeva Reshma Khemchandani Suresh Chandra 0001 Fuzzy linear proximal support vector machines for multi-category data classification. 426-435 2005 67 Neurocomputing https://doi.org/10.1016/j.neucom.2004.09.002 db/journals/ijon/ijon67.html#JayadevaKC05

Jayadeva Reshma Khemchandani Suresh Chandra 0001 Fuzzy Proximal Support Vector Classification Via Generalized Eigenvalues. 360-363 2005 conf/premi/2005 PReMI https://doi.org/10.1007/11590316_54 db/conf/premi/premi2005.html#JayadevaKC05

Jayadeva Reshma Khemchandani Suresh Chandra 0001 Fast and robust learning through fuzzy linear proximal support vector machines. 401-411 2004 61 Neurocomputing https://doi.org/10.1016/j.neucom.2004.02.004 db/journals/ijon/ijon61.html#JayadevaKC04

Anirudh Aggarwal Nadira Ahmadi Pritam Anand Avikant Bhardwaj Amisha Bharti Ankush Bisht Suresh Chandra Suresh Chandra 0001 Arpit Chaudhary Sayanta Chowdhury Anubhav David M. A. Ganaie 0001 Ritesh Gangnani Madan Gopal Keshav Goyal Nishil Gupta Sambhav Jain Jayadeva Anuj Karpatne Vaibhav Khanna Abhijeet Kumar Ankit Kumar M. Arun Kumar Sanjay Kumar Mamta Bhattarai Lamsal Nima Dorji Moktan Sayed Mortaza Dev Nirwal Aman Pal Simran Popli Taniya Rajani Shuvo Saha Roy Hamid Safdari Pooja Saigal Yuan-Hai Shao 0001 Sweta Sharma Muhammad Tanveer 0001Mohammad Tanveer 0001M. Tanveer 0001